{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0748a657-fa89-4630-b3ba-71ed7e93203c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.formula.api as smf\n",
    "import patsy\n",
    "from patsy import builtins as pb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "679c3bd0-10b4-45f0-b472-c3411f9bba9c",
   "metadata": {},
   "source": [
    "## 1. Coeff plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "83d191fc-59cb-4f14-a2bb-381cf7c6a16d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\한승우\\AppData\\Local\\Temp\\ipykernel_1484\\1170952327.py:165: UserWarning: This figure was using a layout engine that is incompatible with subplots_adjust and/or tight_layout; not calling subplots_adjust.\n",
      "  fig.subplots_adjust(wspace=0.55)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2200x620 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\한승우\\AppData\\Local\\Temp\\ipykernel_1484\\1170952327.py:165: UserWarning: This figure was using a layout engine that is incompatible with subplots_adjust and/or tight_layout; not calling subplots_adjust.\n",
      "  fig.subplots_adjust(wspace=0.55)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2200x620 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\한승우\\AppData\\Local\\Temp\\ipykernel_1484\\1170952327.py:165: UserWarning: This figure was using a layout engine that is incompatible with subplots_adjust and/or tight_layout; not calling subplots_adjust.\n",
      "  fig.subplots_adjust(wspace=0.55)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2200x620 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\한승우\\AppData\\Local\\Temp\\ipykernel_1484\\1170952327.py:165: UserWarning: This figure was using a layout engine that is incompatible with subplots_adjust and/or tight_layout; not calling subplots_adjust.\n",
      "  fig.subplots_adjust(wspace=0.55)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2200x620 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\한승우\\AppData\\Local\\Temp\\ipykernel_1484\\1170952327.py:165: UserWarning: This figure was using a layout engine that is incompatible with subplots_adjust and/or tight_layout; not calling subplots_adjust.\n",
      "  fig.subplots_adjust(wspace=0.55)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2200x620 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done. Final figures saved to: E:\\불평등 연구\\데이터\\69_환경_도덕\\_analysis_outputs_main_EP_v1_1row3panels\n"
     ]
    }
   ],
   "source": [
    "# 0) PATH\n",
    "# ============================================================\n",
    "DATA_DIR  = r\"E:\\\"\n",
    "DATA_FILE = os.path.join(DATA_DIR, \"df.xlsx\")\n",
    "\n",
    "OUT_DIR = os.path.join(DATA_DIR, \"_analysis_outputs_main_EP_v1_1row3panels\")\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "df = pd.read_excel(DATA_FILE)\n",
    "\n",
    "# ============================================================\n",
    "# 1) Variables \n",
    "# ============================================================\n",
    "# --- Vignette factors\n",
    "CONSTRAINT = \"C_constraint\"   # 1=High-carbon constrained, 0=Low-carbon feasible\n",
    "SUPPORT    = \"S_support\"      # 1=Support provided, 0=None\n",
    "\n",
    "# --- DVs\n",
    "DVs = {\n",
    "    \"Legitimacy\": \"O1_legitimacy\",\n",
    "    \"Government responsibility\": \"O2_gov_responsibility\",\n",
    "    \"Transition support\": \"O3_transition_support\",\n",
    "    \"Regulatory flexibility\": \"O4_reg_flexibility\",\n",
    "    \"Social essentiality\": \"O5_social_essentiality\",\n",
    "}\n",
    "\n",
    "# ---- Norm index (if already exists, reuse; else compute)\n",
    "norm_items = [\"N1_env_norm\",\"N2_env_norm\",\"N3_env_norm\",\"N4_env_norm\",\"N5_env_norm\"]\n",
    "if \"Norm_index\" in df.columns:\n",
    "    df[\"norm_index\"] = df[\"Norm_index\"]\n",
    "else:\n",
    "    df[\"norm_index\"] = df[norm_items].mean(axis=1)\n",
    "\n",
    "df[\"norm_c\"] = df[\"norm_index\"] - df[\"norm_index\"].mean()\n",
    "\n",
    "# ---- Structural position: low-carbon proximity (0–10 -> 0–1) centered\n",
    "df[\"low_carbon_prox\"] = df[\"low_carbon_proximity_0_10\"] / 10.0\n",
    "df[\"low_carbon_prox\"] = df[\"low_carbon_prox\"].fillna(df[\"low_carbon_prox\"].mean())\n",
    "df[\"struc_c\"] = df[\"low_carbon_prox\"] - df[\"low_carbon_prox\"].mean()\n",
    "\n",
    "# ---- Income band: monthly_income_band 없으면 생성\n",
    "if \"monthly_income_band\" not in df.columns:\n",
    "    if \"income_millionKRW\" in df.columns:\n",
    "        df[\"monthly_income_band\"] = pd.qcut(\n",
    "            df[\"income_millionKRW\"], q=8, labels=[f\"Q{i}\" for i in range(1, 9)]\n",
    "        )\n",
    "    else:\n",
    "        raise ValueError(\"monthly_income_band도 없고 income_millionKRW도 없습니다. 소득 변수명을 확인하세요.\")\n",
    "\n",
    "df[\"union_member\"] = df[\"union_member\"].fillna(\"No\")\n",
    "\n",
    "# df = df[df[\"employment_status\"].isin([\"Employed\",\"Self-employed\"])].copy()\n",
    "\n",
    "# ============================================================\n",
    "# 2) Controls \n",
    "# ============================================================\n",
    "CAT = pb.C  \n",
    "\n",
    "CONTROL_TERMS_MAIN = [\n",
    "    \"age\",\n",
    "    \"CAT(gender)\",\n",
    "    \"CAT(region)\",\n",
    "    \"CAT(education)\",\n",
    "    \"CAT(monthly_income_band)\",\n",
    "    \"CAT(homeowner)\",\n",
    "    \"CAT(marital_status)\",\n",
    "    \"CAT(union_member)\",\n",
    "    \"CAT(employment_type)\",\n",
    "    \"ideology_1_7_last\"\n",
    "]\n",
    "CONTROLS_MAIN = \" + \".join(CONTROL_TERMS_MAIN)\n",
    "\n",
    "# ============================================================\n",
    "# 3) Helpers\n",
    "# ============================================================\n",
    "def fit_ols(formula, data):\n",
    "    # eval_env=1 \n",
    "    return smf.ols(formula, data=data).fit(cov_type=\"HC3\")\n",
    "\n",
    "def short_term_name(term):\n",
    "    return (term.replace(CONSTRAINT, \"C\")\n",
    "                .replace(SUPPORT, \"S\")\n",
    "                .replace(\"norm_c\", \"Norm\")\n",
    "                .replace(\"struc_c\", \"LowCarbonProx\")\n",
    "                .replace(\":\", \"×\"))\n",
    "\n",
    "def extract_terms(model, term_list):\n",
    "    rows = []\n",
    "    for t in term_list:\n",
    "        if t in model.params.index:\n",
    "            rows.append((t,\n",
    "                         float(model.params[t]),\n",
    "                         float(model.bse[t]),\n",
    "                         float(model.pvalues[t])))\n",
    "    return rows\n",
    "\n",
    "def coefpanel(ax, rows, panel_title, xlab=None, panel_tag=\"\"):\n",
    "    if not rows:\n",
    "        ax.axis(\"off\")\n",
    "        return\n",
    "\n",
    "    y = np.arange(len(rows))\n",
    "    coefs = np.array([r[1] for r in rows])\n",
    "    ses   = np.array([r[2] for r in rows])\n",
    "\n",
    "    ci_low  = coefs - 1.96*ses\n",
    "    ci_high = coefs + 1.96*ses\n",
    "\n",
    "    ax.hlines(y, ci_low, ci_high, color=\"black\", linewidth=2)\n",
    "    ax.plot(coefs, y, \"o\", color=\"red\", markersize=9)\n",
    "    ax.axvline(0, color=\"red\", linestyle=\"--\", linewidth=1.6)\n",
    "\n",
    "    labels = [short_term_name(r[0]) for r in rows]\n",
    "    ax.set_yticks(y)\n",
    "    ax.set_yticklabels(labels, fontsize=20)\n",
    "    ax.invert_yaxis()\n",
    "\n",
    "    xmin, xmax = min(ci_low.min(), 0), max(ci_high.max(), 0)\n",
    "    span = xmax - xmin if xmax > xmin else 1.0\n",
    "    ax.set_xlim(xmin - 0.2*span, xmax + 0.2*span)\n",
    "\n",
    "    dx = 0.04 * span\n",
    "    for i, (t, coef, se, p) in enumerate(rows):\n",
    "        txt = f\"{coef:.2f}, p={p:.3f}\"\n",
    "        x_text = coef + (dx if coef >= 0 else -dx)\n",
    "        ax.annotate(\n",
    "            txt,\n",
    "            xy=(x_text, i),\n",
    "            xytext=(0, 3),\n",
    "            textcoords=\"offset points\",\n",
    "            ha=\"left\",\n",
    "            va=\"bottom\",\n",
    "            fontsize=15\n",
    "        )\n",
    "\n",
    "    ax.set_title(f\"{panel_tag} {panel_title}\", fontsize=22, pad=12)\n",
    "    ax.tick_params(axis=\"x\", labelsize=17, pad=6)\n",
    "    ax.tick_params(axis=\"y\", labelsize=20, pad=7)\n",
    "\n",
    "    if xlab:\n",
    "        ax.set_xlabel(xlab, fontsize=18, labelpad=10)\n",
    "\n",
    "# ============================================================\n",
    "# 4) Main Figures (H1–H3)\n",
    "# ============================================================\n",
    "SHOW_CxS = False\n",
    "\n",
    "for dv_label, dv_col in DVs.items():\n",
    "    # (a) Baseline: Constraint, Support\n",
    "    m1 = fit_ols(f\"{dv_col} ~ {CONSTRAINT}*{SUPPORT} + {CONTROLS_MAIN}\", df)\n",
    "    rows_h1 = extract_terms(m1, [CONSTRAINT, SUPPORT] + ([f\"{CONSTRAINT}:{SUPPORT}\"] if SHOW_CxS else []))\n",
    "\n",
    "    # (b) Constraint × Norm\n",
    "    m2 = fit_ols(f\"{dv_col} ~ {CONSTRAINT}*{SUPPORT} + norm_c + {CONSTRAINT}:norm_c + {CONTROLS_MAIN}\", df)\n",
    "    rows_h2 = extract_terms(m2, [f\"{CONSTRAINT}:norm_c\"])\n",
    "\n",
    "    # (c) Constraint × Structural position\n",
    "    m3 = fit_ols(f\"{dv_col} ~ {CONSTRAINT}*{SUPPORT} + struc_c + {CONSTRAINT}:struc_c + {CONTROLS_MAIN}\", df)\n",
    "    rows_h3 = extract_terms(m3, [f\"{CONSTRAINT}:struc_c\"])\n",
    "\n",
    "    fig, axes = plt.subplots(1, 3, figsize=(22, 6.2), constrained_layout=True)\n",
    "    fig.subplots_adjust(wspace=0.55)\n",
    "\n",
    "    coefpanel(axes[0], rows_h1, \"(ATE): Constraint, Support\", \"Coefficient (95% CI)\", \"(a)\")\n",
    "    coefpanel(axes[1], rows_h2, \"Constraint × Norm\", \"Coefficient (95% CI)\", \"(b)\")\n",
    "    coefpanel(axes[2], rows_h3, \"Constraint × LowCarbonProx\", \"Coefficient (95% CI)\", \"(c)\")\n",
    "\n",
    "    outpath = os.path.join(OUT_DIR, f\"DV_{dv_label.replace(' ','_')}_H1H2H3_1row_EP_v1.png\")\n",
    "    plt.savefig(outpath, dpi=600, bbox_inches=\"tight\")\n",
    "    plt.show()\n",
    "    plt.close()\n",
    "\n",
    "print(\"Done. Final figures saved to:\", OUT_DIR)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12f8ef62-3da1-481d-bf8c-2bfb865d6e87",
   "metadata": {},
   "source": [
    "## 2. Full results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a3e54003-97a4-4174-97b2-af49158aed22",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Jupyter display (optional but convenient)\n",
    "from IPython.display import display, Markdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "47886d2e-3525-4bec-902a-d3477c21850d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "## Figure 1 (Legitimacy) — **H1 (Legitimacy)**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Spec A (baseline): DV ~ Constraint*Support + controls**  \n",
       "N = 2,000 | R² = 0.026 | Adj. R² = 0.014"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "*Key terms:*"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>C_constraint</td>\n",
       "      <td>-0.088</td>\n",
       "      <td>0.060</td>\n",
       "      <td>-1.475</td>\n",
       "      <td>0.14</td>\n",
       "      <td></td>\n",
       "      <td>-0.205</td>\n",
       "      <td>0.029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>S_support</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.057</td>\n",
       "      <td>1.822</td>\n",
       "      <td>0.0685</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.008</td>\n",
       "      <td>0.215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>C_constraint:S_support</td>\n",
       "      <td>-0.033</td>\n",
       "      <td>0.086</td>\n",
       "      <td>-0.388</td>\n",
       "      <td>0.698</td>\n",
       "      <td></td>\n",
       "      <td>-0.202</td>\n",
       "      <td>0.136</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      term   coef  se_HC3      t       p sig  ci_low  ci_high\n",
       "21            C_constraint -0.088   0.060 -1.475    0.14      -0.205    0.029\n",
       "22               S_support  0.104   0.057  1.822  0.0685   *  -0.008    0.215\n",
       "23  C_constraint:S_support -0.033   0.086 -0.388   0.698      -0.202    0.136"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Full results — Spec A**  \n",
       "N = 2,000 | R² = 0.026 | Adj. R² = 0.014"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>4.188</td>\n",
       "      <td>0.146</td>\n",
       "      <td>28.708</td>\n",
       "      <td>3.05e-181</td>\n",
       "      <td>***</td>\n",
       "      <td>3.902</td>\n",
       "      <td>4.474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAT(gender)[T.Male]</td>\n",
       "      <td>-0.021</td>\n",
       "      <td>0.043</td>\n",
       "      <td>-0.500</td>\n",
       "      <td>0.617</td>\n",
       "      <td></td>\n",
       "      <td>-0.105</td>\n",
       "      <td>0.063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CAT(region)[T.SeoulMetro]</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.043</td>\n",
       "      <td>-0.022</td>\n",
       "      <td>0.983</td>\n",
       "      <td></td>\n",
       "      <td>-0.086</td>\n",
       "      <td>0.084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAT(education)[T.College2yr]</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.056</td>\n",
       "      <td>2.561</td>\n",
       "      <td>0.0104</td>\n",
       "      <td>**</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAT(education)[T.Grad+]</td>\n",
       "      <td>-0.029</td>\n",
       "      <td>0.076</td>\n",
       "      <td>-0.381</td>\n",
       "      <td>0.703</td>\n",
       "      <td></td>\n",
       "      <td>-0.177</td>\n",
       "      <td>0.119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CAT(education)[T.HighSchool]</td>\n",
       "      <td>0.270</td>\n",
       "      <td>0.065</td>\n",
       "      <td>4.179</td>\n",
       "      <td>2.92e-05</td>\n",
       "      <td>***</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CAT(education)[T.MiddleOrLess]</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.109</td>\n",
       "      <td>1.856</td>\n",
       "      <td>0.0635</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.011</td>\n",
       "      <td>0.416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q2]</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.546</td>\n",
       "      <td></td>\n",
       "      <td>-0.118</td>\n",
       "      <td>0.223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q3]</td>\n",
       "      <td>-0.029</td>\n",
       "      <td>0.086</td>\n",
       "      <td>-0.337</td>\n",
       "      <td>0.736</td>\n",
       "      <td></td>\n",
       "      <td>-0.197</td>\n",
       "      <td>0.140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q4]</td>\n",
       "      <td>-0.035</td>\n",
       "      <td>0.092</td>\n",
       "      <td>-0.383</td>\n",
       "      <td>0.702</td>\n",
       "      <td></td>\n",
       "      <td>-0.215</td>\n",
       "      <td>0.145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q5]</td>\n",
       "      <td>-0.015</td>\n",
       "      <td>0.094</td>\n",
       "      <td>-0.161</td>\n",
       "      <td>0.872</td>\n",
       "      <td></td>\n",
       "      <td>-0.200</td>\n",
       "      <td>0.169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q6]</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.387</td>\n",
       "      <td>0.699</td>\n",
       "      <td></td>\n",
       "      <td>-0.145</td>\n",
       "      <td>0.216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q7]</td>\n",
       "      <td>-0.138</td>\n",
       "      <td>0.095</td>\n",
       "      <td>-1.448</td>\n",
       "      <td>0.148</td>\n",
       "      <td></td>\n",
       "      <td>-0.324</td>\n",
       "      <td>0.049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q8]</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.100</td>\n",
       "      <td>1.034</td>\n",
       "      <td>0.301</td>\n",
       "      <td></td>\n",
       "      <td>-0.093</td>\n",
       "      <td>0.301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CAT(homeowner)[T.1]</td>\n",
       "      <td>-0.048</td>\n",
       "      <td>0.046</td>\n",
       "      <td>-1.041</td>\n",
       "      <td>0.298</td>\n",
       "      <td></td>\n",
       "      <td>-0.138</td>\n",
       "      <td>0.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CAT(marital_status)[T.Other]</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.058</td>\n",
       "      <td>-0.013</td>\n",
       "      <td>0.99</td>\n",
       "      <td></td>\n",
       "      <td>-0.115</td>\n",
       "      <td>0.114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CAT(marital_status)[T.Single]</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.596</td>\n",
       "      <td></td>\n",
       "      <td>-0.074</td>\n",
       "      <td>0.128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAT(union_member)[T.Yes]</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.321</td>\n",
       "      <td>0.748</td>\n",
       "      <td></td>\n",
       "      <td>-0.117</td>\n",
       "      <td>0.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CAT(employment_type)[T.Other]</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.233</td>\n",
       "      <td>0.816</td>\n",
       "      <td></td>\n",
       "      <td>-0.185</td>\n",
       "      <td>0.235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CAT(employment_type)[T.Regular]</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.516</td>\n",
       "      <td>0.606</td>\n",
       "      <td></td>\n",
       "      <td>-0.068</td>\n",
       "      <td>0.116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CAT(employment_type)[T.Self-employed]</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.949</td>\n",
       "      <td></td>\n",
       "      <td>-0.145</td>\n",
       "      <td>0.155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>C_constraint</td>\n",
       "      <td>-0.088</td>\n",
       "      <td>0.060</td>\n",
       "      <td>-1.475</td>\n",
       "      <td>0.14</td>\n",
       "      <td></td>\n",
       "      <td>-0.205</td>\n",
       "      <td>0.029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>S_support</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.057</td>\n",
       "      <td>1.822</td>\n",
       "      <td>0.0685</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.008</td>\n",
       "      <td>0.215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>C_constraint:S_support</td>\n",
       "      <td>-0.033</td>\n",
       "      <td>0.086</td>\n",
       "      <td>-0.388</td>\n",
       "      <td>0.698</td>\n",
       "      <td></td>\n",
       "      <td>-0.202</td>\n",
       "      <td>0.136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>age</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.814</td>\n",
       "      <td>0.416</td>\n",
       "      <td></td>\n",
       "      <td>-0.003</td>\n",
       "      <td>0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ideology_1_7_last</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.889</td>\n",
       "      <td>0.374</td>\n",
       "      <td></td>\n",
       "      <td>-0.022</td>\n",
       "      <td>0.059</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     term   coef  se_HC3       t          p  \\\n",
       "0                               Intercept  4.188   0.146  28.708  3.05e-181   \n",
       "1                     CAT(gender)[T.Male] -0.021   0.043  -0.500      0.617   \n",
       "2               CAT(region)[T.SeoulMetro] -0.001   0.043  -0.022      0.983   \n",
       "3            CAT(education)[T.College2yr]  0.143   0.056   2.561     0.0104   \n",
       "4                 CAT(education)[T.Grad+] -0.029   0.076  -0.381      0.703   \n",
       "5            CAT(education)[T.HighSchool]  0.270   0.065   4.179   2.92e-05   \n",
       "6          CAT(education)[T.MiddleOrLess]  0.202   0.109   1.856     0.0635   \n",
       "7          CAT(monthly_income_band)[T.Q2]  0.052   0.087   0.603      0.546   \n",
       "8          CAT(monthly_income_band)[T.Q3] -0.029   0.086  -0.337      0.736   \n",
       "9          CAT(monthly_income_band)[T.Q4] -0.035   0.092  -0.383      0.702   \n",
       "10         CAT(monthly_income_band)[T.Q5] -0.015   0.094  -0.161      0.872   \n",
       "11         CAT(monthly_income_band)[T.Q6]  0.036   0.092   0.387      0.699   \n",
       "12         CAT(monthly_income_band)[T.Q7] -0.138   0.095  -1.448      0.148   \n",
       "13         CAT(monthly_income_band)[T.Q8]  0.104   0.100   1.034      0.301   \n",
       "14                    CAT(homeowner)[T.1] -0.048   0.046  -1.041      0.298   \n",
       "15           CAT(marital_status)[T.Other] -0.001   0.058  -0.013       0.99   \n",
       "16          CAT(marital_status)[T.Single]  0.027   0.051   0.530      0.596   \n",
       "17               CAT(union_member)[T.Yes]  0.023   0.071   0.321      0.748   \n",
       "18          CAT(employment_type)[T.Other]  0.025   0.107   0.233      0.816   \n",
       "19        CAT(employment_type)[T.Regular]  0.024   0.047   0.516      0.606   \n",
       "20  CAT(employment_type)[T.Self-employed]  0.005   0.077   0.064      0.949   \n",
       "21                           C_constraint -0.088   0.060  -1.475       0.14   \n",
       "22                              S_support  0.104   0.057   1.822     0.0685   \n",
       "23                 C_constraint:S_support -0.033   0.086  -0.388      0.698   \n",
       "24                                    age  0.002   0.003   0.814      0.416   \n",
       "25                      ideology_1_7_last  0.018   0.021   0.889      0.374   \n",
       "\n",
       "    sig  ci_low  ci_high  \n",
       "0   ***   3.902    4.474  \n",
       "1        -0.105    0.063  \n",
       "2        -0.086    0.084  \n",
       "3    **   0.034    0.252  \n",
       "4        -0.177    0.119  \n",
       "5   ***   0.143    0.397  \n",
       "6     *  -0.011    0.416  \n",
       "7        -0.118    0.223  \n",
       "8        -0.197    0.140  \n",
       "9        -0.215    0.145  \n",
       "10       -0.200    0.169  \n",
       "11       -0.145    0.216  \n",
       "12       -0.324    0.049  \n",
       "13       -0.093    0.301  \n",
       "14       -0.138    0.042  \n",
       "15       -0.115    0.114  \n",
       "16       -0.074    0.128  \n",
       "17       -0.117    0.163  \n",
       "18       -0.185    0.235  \n",
       "19       -0.068    0.116  \n",
       "20       -0.145    0.155  \n",
       "21       -0.205    0.029  \n",
       "22    *  -0.008    0.215  \n",
       "23       -0.202    0.136  \n",
       "24       -0.003    0.007  \n",
       "25       -0.022    0.059  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Spec B (Norm moderation): + Norm + Constraint×Norm**  \n",
       "N = 2,000 | R² = 0.116 | Adj. R² = 0.103"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "*Key terms:*"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>C_constraint</td>\n",
       "      <td>-0.080</td>\n",
       "      <td>0.057</td>\n",
       "      <td>-1.402</td>\n",
       "      <td>0.161</td>\n",
       "      <td></td>\n",
       "      <td>-0.191</td>\n",
       "      <td>0.032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>S_support</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.056</td>\n",
       "      <td>1.786</td>\n",
       "      <td>0.0741</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.010</td>\n",
       "      <td>0.211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>C_constraint:S_support</td>\n",
       "      <td>-0.045</td>\n",
       "      <td>0.082</td>\n",
       "      <td>-0.545</td>\n",
       "      <td>0.585</td>\n",
       "      <td></td>\n",
       "      <td>-0.206</td>\n",
       "      <td>0.116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>C_constraint:norm_c</td>\n",
       "      <td>-0.371</td>\n",
       "      <td>0.036</td>\n",
       "      <td>-10.447</td>\n",
       "      <td>1.52e-25</td>\n",
       "      <td>***</td>\n",
       "      <td>-0.440</td>\n",
       "      <td>-0.301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      term   coef  se_HC3       t         p  sig  ci_low  \\\n",
       "21            C_constraint -0.080   0.057  -1.402     0.161       -0.191   \n",
       "22               S_support  0.101   0.056   1.786    0.0741    *  -0.010   \n",
       "23  C_constraint:S_support -0.045   0.082  -0.545     0.585       -0.206   \n",
       "25     C_constraint:norm_c -0.371   0.036 -10.447  1.52e-25  ***  -0.440   \n",
       "\n",
       "    ci_high  \n",
       "21    0.032  \n",
       "22    0.211  \n",
       "23    0.116  \n",
       "25   -0.301  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Full results — Spec B**  \n",
       "N = 2,000 | R² = 0.116 | Adj. R² = 0.103"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
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    },
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>4.427</td>\n",
       "      <td>0.140</td>\n",
       "      <td>31.650</td>\n",
       "      <td>7.51e-220</td>\n",
       "      <td>***</td>\n",
       "      <td>4.152</td>\n",
       "      <td>4.701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAT(gender)[T.Male]</td>\n",
       "      <td>-0.047</td>\n",
       "      <td>0.041</td>\n",
       "      <td>-1.139</td>\n",
       "      <td>0.255</td>\n",
       "      <td></td>\n",
       "      <td>-0.127</td>\n",
       "      <td>0.034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CAT(region)[T.SeoulMetro]</td>\n",
       "      <td>-0.011</td>\n",
       "      <td>0.041</td>\n",
       "      <td>-0.264</td>\n",
       "      <td>0.791</td>\n",
       "      <td></td>\n",
       "      <td>-0.092</td>\n",
       "      <td>0.070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAT(education)[T.College2yr]</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.747</td>\n",
       "      <td>0.455</td>\n",
       "      <td></td>\n",
       "      <td>-0.066</td>\n",
       "      <td>0.146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAT(education)[T.Grad+]</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.072</td>\n",
       "      <td>1.047</td>\n",
       "      <td>0.295</td>\n",
       "      <td></td>\n",
       "      <td>-0.066</td>\n",
       "      <td>0.218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CAT(education)[T.HighSchool]</td>\n",
       "      <td>0.086</td>\n",
       "      <td>0.063</td>\n",
       "      <td>1.363</td>\n",
       "      <td>0.173</td>\n",
       "      <td></td>\n",
       "      <td>-0.038</td>\n",
       "      <td>0.210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CAT(education)[T.MiddleOrLess]</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.903</td>\n",
       "      <td></td>\n",
       "      <td>-0.197</td>\n",
       "      <td>0.223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q2]</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.799</td>\n",
       "      <td></td>\n",
       "      <td>-0.142</td>\n",
       "      <td>0.185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q3]</td>\n",
       "      <td>-0.038</td>\n",
       "      <td>0.082</td>\n",
       "      <td>-0.461</td>\n",
       "      <td>0.645</td>\n",
       "      <td></td>\n",
       "      <td>-0.199</td>\n",
       "      <td>0.124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q4]</td>\n",
       "      <td>-0.049</td>\n",
       "      <td>0.087</td>\n",
       "      <td>-0.561</td>\n",
       "      <td>0.575</td>\n",
       "      <td></td>\n",
       "      <td>-0.219</td>\n",
       "      <td>0.121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q5]</td>\n",
       "      <td>-0.021</td>\n",
       "      <td>0.089</td>\n",
       "      <td>-0.234</td>\n",
       "      <td>0.815</td>\n",
       "      <td></td>\n",
       "      <td>-0.195</td>\n",
       "      <td>0.153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q6]</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.921</td>\n",
       "      <td></td>\n",
       "      <td>-0.162</td>\n",
       "      <td>0.179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q7]</td>\n",
       "      <td>-0.141</td>\n",
       "      <td>0.090</td>\n",
       "      <td>-1.554</td>\n",
       "      <td>0.12</td>\n",
       "      <td></td>\n",
       "      <td>-0.318</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q8]</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.949</td>\n",
       "      <td>0.342</td>\n",
       "      <td></td>\n",
       "      <td>-0.096</td>\n",
       "      <td>0.275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CAT(homeowner)[T.1]</td>\n",
       "      <td>-0.043</td>\n",
       "      <td>0.044</td>\n",
       "      <td>-0.985</td>\n",
       "      <td>0.325</td>\n",
       "      <td></td>\n",
       "      <td>-0.130</td>\n",
       "      <td>0.043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CAT(marital_status)[T.Other]</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.285</td>\n",
       "      <td>0.776</td>\n",
       "      <td></td>\n",
       "      <td>-0.092</td>\n",
       "      <td>0.123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CAT(marital_status)[T.Single]</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.858</td>\n",
       "      <td></td>\n",
       "      <td>-0.088</td>\n",
       "      <td>0.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAT(union_member)[T.Yes]</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.382</td>\n",
       "      <td>0.702</td>\n",
       "      <td></td>\n",
       "      <td>-0.107</td>\n",
       "      <td>0.158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CAT(employment_type)[T.Other]</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.684</td>\n",
       "      <td>0.494</td>\n",
       "      <td></td>\n",
       "      <td>-0.128</td>\n",
       "      <td>0.266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CAT(employment_type)[T.Regular]</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.201</td>\n",
       "      <td>0.84</td>\n",
       "      <td></td>\n",
       "      <td>-0.079</td>\n",
       "      <td>0.097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CAT(employment_type)[T.Self-employed]</td>\n",
       "      <td>-0.014</td>\n",
       "      <td>0.073</td>\n",
       "      <td>-0.196</td>\n",
       "      <td>0.845</td>\n",
       "      <td></td>\n",
       "      <td>-0.158</td>\n",
       "      <td>0.129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>C_constraint</td>\n",
       "      <td>-0.080</td>\n",
       "      <td>0.057</td>\n",
       "      <td>-1.402</td>\n",
       "      <td>0.161</td>\n",
       "      <td></td>\n",
       "      <td>-0.191</td>\n",
       "      <td>0.032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>S_support</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.056</td>\n",
       "      <td>1.786</td>\n",
       "      <td>0.0741</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.010</td>\n",
       "      <td>0.211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>C_constraint:S_support</td>\n",
       "      <td>-0.045</td>\n",
       "      <td>0.082</td>\n",
       "      <td>-0.545</td>\n",
       "      <td>0.585</td>\n",
       "      <td></td>\n",
       "      <td>-0.206</td>\n",
       "      <td>0.116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>norm_c</td>\n",
       "      <td>-0.016</td>\n",
       "      <td>0.024</td>\n",
       "      <td>-0.660</td>\n",
       "      <td>0.509</td>\n",
       "      <td></td>\n",
       "      <td>-0.064</td>\n",
       "      <td>0.032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>C_constraint:norm_c</td>\n",
       "      <td>-0.371</td>\n",
       "      <td>0.036</td>\n",
       "      <td>-10.447</td>\n",
       "      <td>1.52e-25</td>\n",
       "      <td>***</td>\n",
       "      <td>-0.440</td>\n",
       "      <td>-0.301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>age</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.963</td>\n",
       "      <td></td>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ideology_1_7_last</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.240</td>\n",
       "      <td>0.81</td>\n",
       "      <td></td>\n",
       "      <td>-0.034</td>\n",
       "      <td>0.043</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     term   coef  se_HC3       t          p  \\\n",
       "0                               Intercept  4.427   0.140  31.650  7.51e-220   \n",
       "1                     CAT(gender)[T.Male] -0.047   0.041  -1.139      0.255   \n",
       "2               CAT(region)[T.SeoulMetro] -0.011   0.041  -0.264      0.791   \n",
       "3            CAT(education)[T.College2yr]  0.040   0.054   0.747      0.455   \n",
       "4                 CAT(education)[T.Grad+]  0.076   0.072   1.047      0.295   \n",
       "5            CAT(education)[T.HighSchool]  0.086   0.063   1.363      0.173   \n",
       "6          CAT(education)[T.MiddleOrLess]  0.013   0.107   0.122      0.903   \n",
       "7          CAT(monthly_income_band)[T.Q2]  0.021   0.083   0.254      0.799   \n",
       "8          CAT(monthly_income_band)[T.Q3] -0.038   0.082  -0.461      0.645   \n",
       "9          CAT(monthly_income_band)[T.Q4] -0.049   0.087  -0.561      0.575   \n",
       "10         CAT(monthly_income_band)[T.Q5] -0.021   0.089  -0.234      0.815   \n",
       "11         CAT(monthly_income_band)[T.Q6]  0.009   0.087   0.099      0.921   \n",
       "12         CAT(monthly_income_band)[T.Q7] -0.141   0.090  -1.554       0.12   \n",
       "13         CAT(monthly_income_band)[T.Q8]  0.090   0.095   0.949      0.342   \n",
       "14                    CAT(homeowner)[T.1] -0.043   0.044  -0.985      0.325   \n",
       "15           CAT(marital_status)[T.Other]  0.016   0.055   0.285      0.776   \n",
       "16          CAT(marital_status)[T.Single]  0.009   0.049   0.178      0.858   \n",
       "17               CAT(union_member)[T.Yes]  0.026   0.068   0.382      0.702   \n",
       "18          CAT(employment_type)[T.Other]  0.069   0.101   0.684      0.494   \n",
       "19        CAT(employment_type)[T.Regular]  0.009   0.045   0.201       0.84   \n",
       "20  CAT(employment_type)[T.Self-employed] -0.014   0.073  -0.196      0.845   \n",
       "21                           C_constraint -0.080   0.057  -1.402      0.161   \n",
       "22                              S_support  0.101   0.056   1.786     0.0741   \n",
       "23                 C_constraint:S_support -0.045   0.082  -0.545      0.585   \n",
       "24                                 norm_c -0.016   0.024  -0.660      0.509   \n",
       "25                    C_constraint:norm_c -0.371   0.036 -10.447   1.52e-25   \n",
       "26                                    age  0.000   0.002   0.046      0.963   \n",
       "27                      ideology_1_7_last  0.005   0.020   0.240       0.81   \n",
       "\n",
       "    sig  ci_low  ci_high  \n",
       "0   ***   4.152    4.701  \n",
       "1        -0.127    0.034  \n",
       "2        -0.092    0.070  \n",
       "3        -0.066    0.146  \n",
       "4        -0.066    0.218  \n",
       "5        -0.038    0.210  \n",
       "6        -0.197    0.223  \n",
       "7        -0.142    0.185  \n",
       "8        -0.199    0.124  \n",
       "9        -0.219    0.121  \n",
       "10       -0.195    0.153  \n",
       "11       -0.162    0.179  \n",
       "12       -0.318    0.037  \n",
       "13       -0.096    0.275  \n",
       "14       -0.130    0.043  \n",
       "15       -0.092    0.123  \n",
       "16       -0.088    0.106  \n",
       "17       -0.107    0.158  \n",
       "18       -0.128    0.266  \n",
       "19       -0.079    0.097  \n",
       "20       -0.158    0.129  \n",
       "21       -0.191    0.032  \n",
       "22    *  -0.010    0.211  \n",
       "23       -0.206    0.116  \n",
       "24       -0.064    0.032  \n",
       "25  ***  -0.440   -0.301  \n",
       "26       -0.005    0.005  \n",
       "27       -0.034    0.043  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Spec C (Structural moderation): + LowCarbonProx + Constraint×LowCarbonProx**  \n",
       "N = 2,000 | R² = 0.113 | Adj. R² = 0.101"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "*Key terms:*"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>C_constraint</td>\n",
       "      <td>-0.104</td>\n",
       "      <td>0.058</td>\n",
       "      <td>-1.814</td>\n",
       "      <td>0.0696</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.217</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>S_support</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.057</td>\n",
       "      <td>1.796</td>\n",
       "      <td>0.0725</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>0.214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>C_constraint:S_support</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.914</td>\n",
       "      <td></td>\n",
       "      <td>-0.152</td>\n",
       "      <td>0.170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>C_constraint:struc_c</td>\n",
       "      <td>-2.111</td>\n",
       "      <td>0.205</td>\n",
       "      <td>-10.313</td>\n",
       "      <td>6.15e-25</td>\n",
       "      <td>***</td>\n",
       "      <td>-2.512</td>\n",
       "      <td>-1.710</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      term   coef  se_HC3       t         p  sig  ci_low  \\\n",
       "21            C_constraint -0.104   0.058  -1.814    0.0696    *  -0.217   \n",
       "22               S_support  0.102   0.057   1.796    0.0725    *  -0.009   \n",
       "23  C_constraint:S_support  0.009   0.082   0.109     0.914       -0.152   \n",
       "25    C_constraint:struc_c -2.111   0.205 -10.313  6.15e-25  ***  -2.512   \n",
       "\n",
       "    ci_high  \n",
       "21    0.008  \n",
       "22    0.214  \n",
       "23    0.170  \n",
       "25   -1.710  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Full results — Spec C**  \n",
       "N = 2,000 | R² = 0.113 | Adj. R² = 0.101"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
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    },
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>4.212</td>\n",
       "      <td>0.138</td>\n",
       "      <td>30.471</td>\n",
       "      <td>6.3e-204</td>\n",
       "      <td>***</td>\n",
       "      <td>3.941</td>\n",
       "      <td>4.483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAT(gender)[T.Male]</td>\n",
       "      <td>-0.012</td>\n",
       "      <td>0.041</td>\n",
       "      <td>-0.282</td>\n",
       "      <td>0.778</td>\n",
       "      <td></td>\n",
       "      <td>-0.092</td>\n",
       "      <td>0.069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CAT(region)[T.SeoulMetro]</td>\n",
       "      <td>-0.015</td>\n",
       "      <td>0.041</td>\n",
       "      <td>-0.354</td>\n",
       "      <td>0.724</td>\n",
       "      <td></td>\n",
       "      <td>-0.096</td>\n",
       "      <td>0.066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAT(education)[T.College2yr]</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.053</td>\n",
       "      <td>1.861</td>\n",
       "      <td>0.0628</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAT(education)[T.Grad+]</td>\n",
       "      <td>-0.059</td>\n",
       "      <td>0.073</td>\n",
       "      <td>-0.806</td>\n",
       "      <td>0.42</td>\n",
       "      <td></td>\n",
       "      <td>-0.201</td>\n",
       "      <td>0.084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CAT(education)[T.HighSchool]</td>\n",
       "      <td>0.232</td>\n",
       "      <td>0.061</td>\n",
       "      <td>3.797</td>\n",
       "      <td>0.000147</td>\n",
       "      <td>***</td>\n",
       "      <td>0.112</td>\n",
       "      <td>0.352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CAT(education)[T.MiddleOrLess]</td>\n",
       "      <td>0.168</td>\n",
       "      <td>0.106</td>\n",
       "      <td>1.593</td>\n",
       "      <td>0.111</td>\n",
       "      <td></td>\n",
       "      <td>-0.039</td>\n",
       "      <td>0.375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q2]</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.589</td>\n",
       "      <td>0.556</td>\n",
       "      <td></td>\n",
       "      <td>-0.114</td>\n",
       "      <td>0.212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q3]</td>\n",
       "      <td>-0.051</td>\n",
       "      <td>0.084</td>\n",
       "      <td>-0.612</td>\n",
       "      <td>0.541</td>\n",
       "      <td></td>\n",
       "      <td>-0.215</td>\n",
       "      <td>0.113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q4]</td>\n",
       "      <td>-0.045</td>\n",
       "      <td>0.087</td>\n",
       "      <td>-0.509</td>\n",
       "      <td>0.611</td>\n",
       "      <td></td>\n",
       "      <td>-0.216</td>\n",
       "      <td>0.127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q5]</td>\n",
       "      <td>-0.022</td>\n",
       "      <td>0.088</td>\n",
       "      <td>-0.252</td>\n",
       "      <td>0.801</td>\n",
       "      <td></td>\n",
       "      <td>-0.196</td>\n",
       "      <td>0.151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q6]</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.091</td>\n",
       "      <td>0.236</td>\n",
       "      <td>0.814</td>\n",
       "      <td></td>\n",
       "      <td>-0.156</td>\n",
       "      <td>0.199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q7]</td>\n",
       "      <td>-0.118</td>\n",
       "      <td>0.091</td>\n",
       "      <td>-1.286</td>\n",
       "      <td>0.198</td>\n",
       "      <td></td>\n",
       "      <td>-0.297</td>\n",
       "      <td>0.062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q8]</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.097</td>\n",
       "      <td>0.830</td>\n",
       "      <td>0.406</td>\n",
       "      <td></td>\n",
       "      <td>-0.109</td>\n",
       "      <td>0.270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CAT(homeowner)[T.1]</td>\n",
       "      <td>-0.050</td>\n",
       "      <td>0.044</td>\n",
       "      <td>-1.124</td>\n",
       "      <td>0.261</td>\n",
       "      <td></td>\n",
       "      <td>-0.136</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CAT(marital_status)[T.Other]</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.723</td>\n",
       "      <td></td>\n",
       "      <td>-0.088</td>\n",
       "      <td>0.127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CAT(marital_status)[T.Single]</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.554</td>\n",
       "      <td></td>\n",
       "      <td>-0.067</td>\n",
       "      <td>0.126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAT(union_member)[T.Yes]</td>\n",
       "      <td>-0.026</td>\n",
       "      <td>0.068</td>\n",
       "      <td>-0.390</td>\n",
       "      <td>0.697</td>\n",
       "      <td></td>\n",
       "      <td>-0.159</td>\n",
       "      <td>0.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CAT(employment_type)[T.Other]</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.106</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.903</td>\n",
       "      <td></td>\n",
       "      <td>-0.196</td>\n",
       "      <td>0.222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CAT(employment_type)[T.Regular]</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.579</td>\n",
       "      <td></td>\n",
       "      <td>-0.063</td>\n",
       "      <td>0.112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CAT(employment_type)[T.Self-employed]</td>\n",
       "      <td>-0.003</td>\n",
       "      <td>0.073</td>\n",
       "      <td>-0.036</td>\n",
       "      <td>0.972</td>\n",
       "      <td></td>\n",
       "      <td>-0.146</td>\n",
       "      <td>0.141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>C_constraint</td>\n",
       "      <td>-0.104</td>\n",
       "      <td>0.058</td>\n",
       "      <td>-1.814</td>\n",
       "      <td>0.0696</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.217</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>S_support</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.057</td>\n",
       "      <td>1.796</td>\n",
       "      <td>0.0725</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>0.214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>C_constraint:S_support</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.914</td>\n",
       "      <td></td>\n",
       "      <td>-0.152</td>\n",
       "      <td>0.170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>struc_c</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.140</td>\n",
       "      <td>0.281</td>\n",
       "      <td>0.779</td>\n",
       "      <td></td>\n",
       "      <td>-0.235</td>\n",
       "      <td>0.314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>C_constraint:struc_c</td>\n",
       "      <td>-2.111</td>\n",
       "      <td>0.205</td>\n",
       "      <td>-10.313</td>\n",
       "      <td>6.15e-25</td>\n",
       "      <td>***</td>\n",
       "      <td>-2.512</td>\n",
       "      <td>-1.710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>age</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>1.091</td>\n",
       "      <td>0.275</td>\n",
       "      <td></td>\n",
       "      <td>-0.002</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ideology_1_7_last</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.786</td>\n",
       "      <td>0.432</td>\n",
       "      <td></td>\n",
       "      <td>-0.023</td>\n",
       "      <td>0.053</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     term   coef  se_HC3       t         p  \\\n",
       "0                               Intercept  4.212   0.138  30.471  6.3e-204   \n",
       "1                     CAT(gender)[T.Male] -0.012   0.041  -0.282     0.778   \n",
       "2               CAT(region)[T.SeoulMetro] -0.015   0.041  -0.354     0.724   \n",
       "3            CAT(education)[T.College2yr]  0.099   0.053   1.861    0.0628   \n",
       "4                 CAT(education)[T.Grad+] -0.059   0.073  -0.806      0.42   \n",
       "5            CAT(education)[T.HighSchool]  0.232   0.061   3.797  0.000147   \n",
       "6          CAT(education)[T.MiddleOrLess]  0.168   0.106   1.593     0.111   \n",
       "7          CAT(monthly_income_band)[T.Q2]  0.049   0.083   0.589     0.556   \n",
       "8          CAT(monthly_income_band)[T.Q3] -0.051   0.084  -0.612     0.541   \n",
       "9          CAT(monthly_income_band)[T.Q4] -0.045   0.087  -0.509     0.611   \n",
       "10         CAT(monthly_income_band)[T.Q5] -0.022   0.088  -0.252     0.801   \n",
       "11         CAT(monthly_income_band)[T.Q6]  0.021   0.091   0.236     0.814   \n",
       "12         CAT(monthly_income_band)[T.Q7] -0.118   0.091  -1.286     0.198   \n",
       "13         CAT(monthly_income_band)[T.Q8]  0.080   0.097   0.830     0.406   \n",
       "14                    CAT(homeowner)[T.1] -0.050   0.044  -1.124     0.261   \n",
       "15           CAT(marital_status)[T.Other]  0.019   0.055   0.354     0.723   \n",
       "16          CAT(marital_status)[T.Single]  0.029   0.049   0.591     0.554   \n",
       "17               CAT(union_member)[T.Yes] -0.026   0.068  -0.390     0.697   \n",
       "18          CAT(employment_type)[T.Other]  0.013   0.106   0.122     0.903   \n",
       "19        CAT(employment_type)[T.Regular]  0.025   0.045   0.556     0.579   \n",
       "20  CAT(employment_type)[T.Self-employed] -0.003   0.073  -0.036     0.972   \n",
       "21                           C_constraint -0.104   0.058  -1.814    0.0696   \n",
       "22                              S_support  0.102   0.057   1.796    0.0725   \n",
       "23                 C_constraint:S_support  0.009   0.082   0.109     0.914   \n",
       "24                                struc_c  0.039   0.140   0.281     0.779   \n",
       "25                   C_constraint:struc_c -2.111   0.205 -10.313  6.15e-25   \n",
       "26                                    age  0.003   0.002   1.091     0.275   \n",
       "27                      ideology_1_7_last  0.015   0.019   0.786     0.432   \n",
       "\n",
       "    sig  ci_low  ci_high  \n",
       "0   ***   3.941    4.483  \n",
       "1        -0.092    0.069  \n",
       "2        -0.096    0.066  \n",
       "3     *  -0.005    0.203  \n",
       "4        -0.201    0.084  \n",
       "5   ***   0.112    0.352  \n",
       "6        -0.039    0.375  \n",
       "7        -0.114    0.212  \n",
       "8        -0.215    0.113  \n",
       "9        -0.216    0.127  \n",
       "10       -0.196    0.151  \n",
       "11       -0.156    0.199  \n",
       "12       -0.297    0.062  \n",
       "13       -0.109    0.270  \n",
       "14       -0.136    0.037  \n",
       "15       -0.088    0.127  \n",
       "16       -0.067    0.126  \n",
       "17       -0.159    0.106  \n",
       "18       -0.196    0.222  \n",
       "19       -0.063    0.112  \n",
       "20       -0.146    0.141  \n",
       "21    *  -0.217    0.008  \n",
       "22    *  -0.009    0.214  \n",
       "23       -0.152    0.170  \n",
       "24       -0.235    0.314  \n",
       "25  ***  -2.512   -1.710  \n",
       "26       -0.002    0.008  \n",
       "27       -0.023    0.053  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## Figure 2 (Government responsibility) — **H2 (Government responsibility)**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Spec A (baseline): DV ~ Constraint*Support + controls**  \n",
       "N = 2,000 | R² = 0.198 | Adj. R² = 0.188"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "*Key terms:*"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>C_constraint</td>\n",
       "      <td>0.097</td>\n",
       "      <td>0.051</td>\n",
       "      <td>1.903</td>\n",
       "      <td>0.057</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.003</td>\n",
       "      <td>0.196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>S_support</td>\n",
       "      <td>0.711</td>\n",
       "      <td>0.051</td>\n",
       "      <td>13.987</td>\n",
       "      <td>1.87e-44</td>\n",
       "      <td>***</td>\n",
       "      <td>0.611</td>\n",
       "      <td>0.811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>C_constraint:S_support</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.447</td>\n",
       "      <td></td>\n",
       "      <td>-0.085</td>\n",
       "      <td>0.193</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      term   coef  se_HC3       t         p  sig  ci_low  \\\n",
       "21            C_constraint  0.097   0.051   1.903     0.057    *  -0.003   \n",
       "22               S_support  0.711   0.051  13.987  1.87e-44  ***   0.611   \n",
       "23  C_constraint:S_support  0.054   0.071   0.760     0.447       -0.085   \n",
       "\n",
       "    ci_high  \n",
       "21    0.196  \n",
       "22    0.811  \n",
       "23    0.193  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Full results — Spec A**  \n",
       "N = 2,000 | R² = 0.198 | Adj. R² = 0.188"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
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    },
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>4.087</td>\n",
       "      <td>0.116</td>\n",
       "      <td>35.162</td>\n",
       "      <td>7.52e-271</td>\n",
       "      <td>***</td>\n",
       "      <td>3.859</td>\n",
       "      <td>4.315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAT(gender)[T.Male]</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.035</td>\n",
       "      <td>-0.022</td>\n",
       "      <td>0.983</td>\n",
       "      <td></td>\n",
       "      <td>-0.070</td>\n",
       "      <td>0.069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CAT(region)[T.SeoulMetro]</td>\n",
       "      <td>-0.058</td>\n",
       "      <td>0.035</td>\n",
       "      <td>-1.642</td>\n",
       "      <td>0.101</td>\n",
       "      <td></td>\n",
       "      <td>-0.128</td>\n",
       "      <td>0.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAT(education)[T.College2yr]</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.046</td>\n",
       "      <td>1.793</td>\n",
       "      <td>0.073</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.008</td>\n",
       "      <td>0.172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAT(education)[T.Grad+]</td>\n",
       "      <td>-0.119</td>\n",
       "      <td>0.068</td>\n",
       "      <td>-1.743</td>\n",
       "      <td>0.0813</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.252</td>\n",
       "      <td>0.015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CAT(education)[T.HighSchool]</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.052</td>\n",
       "      <td>1.200</td>\n",
       "      <td>0.23</td>\n",
       "      <td></td>\n",
       "      <td>-0.040</td>\n",
       "      <td>0.164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CAT(education)[T.MiddleOrLess]</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.573</td>\n",
       "      <td>0.566</td>\n",
       "      <td></td>\n",
       "      <td>-0.114</td>\n",
       "      <td>0.208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q2]</td>\n",
       "      <td>-0.030</td>\n",
       "      <td>0.072</td>\n",
       "      <td>-0.414</td>\n",
       "      <td>0.679</td>\n",
       "      <td></td>\n",
       "      <td>-0.170</td>\n",
       "      <td>0.111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q3]</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.070</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.982</td>\n",
       "      <td></td>\n",
       "      <td>-0.136</td>\n",
       "      <td>0.139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q4]</td>\n",
       "      <td>-0.020</td>\n",
       "      <td>0.075</td>\n",
       "      <td>-0.264</td>\n",
       "      <td>0.792</td>\n",
       "      <td></td>\n",
       "      <td>-0.166</td>\n",
       "      <td>0.126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q5]</td>\n",
       "      <td>-0.089</td>\n",
       "      <td>0.075</td>\n",
       "      <td>-1.187</td>\n",
       "      <td>0.235</td>\n",
       "      <td></td>\n",
       "      <td>-0.236</td>\n",
       "      <td>0.058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q6]</td>\n",
       "      <td>-0.073</td>\n",
       "      <td>0.074</td>\n",
       "      <td>-0.981</td>\n",
       "      <td>0.326</td>\n",
       "      <td></td>\n",
       "      <td>-0.218</td>\n",
       "      <td>0.073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q7]</td>\n",
       "      <td>-0.104</td>\n",
       "      <td>0.077</td>\n",
       "      <td>-1.339</td>\n",
       "      <td>0.181</td>\n",
       "      <td></td>\n",
       "      <td>-0.255</td>\n",
       "      <td>0.048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q8]</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.172</td>\n",
       "      <td>0.864</td>\n",
       "      <td></td>\n",
       "      <td>-0.147</td>\n",
       "      <td>0.175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CAT(homeowner)[T.1]</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.038</td>\n",
       "      <td>1.159</td>\n",
       "      <td>0.246</td>\n",
       "      <td></td>\n",
       "      <td>-0.031</td>\n",
       "      <td>0.120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CAT(marital_status)[T.Other]</td>\n",
       "      <td>-0.062</td>\n",
       "      <td>0.048</td>\n",
       "      <td>-1.283</td>\n",
       "      <td>0.2</td>\n",
       "      <td></td>\n",
       "      <td>-0.157</td>\n",
       "      <td>0.033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CAT(marital_status)[T.Single]</td>\n",
       "      <td>-0.071</td>\n",
       "      <td>0.044</td>\n",
       "      <td>-1.635</td>\n",
       "      <td>0.102</td>\n",
       "      <td></td>\n",
       "      <td>-0.157</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAT(union_member)[T.Yes]</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.285</td>\n",
       "      <td>0.776</td>\n",
       "      <td></td>\n",
       "      <td>-0.095</td>\n",
       "      <td>0.128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CAT(employment_type)[T.Other]</td>\n",
       "      <td>-0.040</td>\n",
       "      <td>0.088</td>\n",
       "      <td>-0.449</td>\n",
       "      <td>0.653</td>\n",
       "      <td></td>\n",
       "      <td>-0.212</td>\n",
       "      <td>0.133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CAT(employment_type)[T.Regular]</td>\n",
       "      <td>-0.040</td>\n",
       "      <td>0.039</td>\n",
       "      <td>-1.032</td>\n",
       "      <td>0.302</td>\n",
       "      <td></td>\n",
       "      <td>-0.117</td>\n",
       "      <td>0.036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CAT(employment_type)[T.Self-employed]</td>\n",
       "      <td>-0.082</td>\n",
       "      <td>0.063</td>\n",
       "      <td>-1.310</td>\n",
       "      <td>0.19</td>\n",
       "      <td></td>\n",
       "      <td>-0.205</td>\n",
       "      <td>0.041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>C_constraint</td>\n",
       "      <td>0.097</td>\n",
       "      <td>0.051</td>\n",
       "      <td>1.903</td>\n",
       "      <td>0.057</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.003</td>\n",
       "      <td>0.196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>S_support</td>\n",
       "      <td>0.711</td>\n",
       "      <td>0.051</td>\n",
       "      <td>13.987</td>\n",
       "      <td>1.87e-44</td>\n",
       "      <td>***</td>\n",
       "      <td>0.611</td>\n",
       "      <td>0.811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>C_constraint:S_support</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.447</td>\n",
       "      <td></td>\n",
       "      <td>-0.085</td>\n",
       "      <td>0.193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>age</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.002</td>\n",
       "      <td>-0.265</td>\n",
       "      <td>0.791</td>\n",
       "      <td></td>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ideology_1_7_last</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.017</td>\n",
       "      <td>1.422</td>\n",
       "      <td>0.155</td>\n",
       "      <td></td>\n",
       "      <td>-0.009</td>\n",
       "      <td>0.057</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     term   coef  se_HC3       t          p  \\\n",
       "0                               Intercept  4.087   0.116  35.162  7.52e-271   \n",
       "1                     CAT(gender)[T.Male] -0.001   0.035  -0.022      0.983   \n",
       "2               CAT(region)[T.SeoulMetro] -0.058   0.035  -1.642      0.101   \n",
       "3            CAT(education)[T.College2yr]  0.082   0.046   1.793      0.073   \n",
       "4                 CAT(education)[T.Grad+] -0.119   0.068  -1.743     0.0813   \n",
       "5            CAT(education)[T.HighSchool]  0.062   0.052   1.200       0.23   \n",
       "6          CAT(education)[T.MiddleOrLess]  0.047   0.082   0.573      0.566   \n",
       "7          CAT(monthly_income_band)[T.Q2] -0.030   0.072  -0.414      0.679   \n",
       "8          CAT(monthly_income_band)[T.Q3]  0.002   0.070   0.023      0.982   \n",
       "9          CAT(monthly_income_band)[T.Q4] -0.020   0.075  -0.264      0.792   \n",
       "10         CAT(monthly_income_band)[T.Q5] -0.089   0.075  -1.187      0.235   \n",
       "11         CAT(monthly_income_band)[T.Q6] -0.073   0.074  -0.981      0.326   \n",
       "12         CAT(monthly_income_band)[T.Q7] -0.104   0.077  -1.339      0.181   \n",
       "13         CAT(monthly_income_band)[T.Q8]  0.014   0.082   0.172      0.864   \n",
       "14                    CAT(homeowner)[T.1]  0.044   0.038   1.159      0.246   \n",
       "15           CAT(marital_status)[T.Other] -0.062   0.048  -1.283        0.2   \n",
       "16          CAT(marital_status)[T.Single] -0.071   0.044  -1.635      0.102   \n",
       "17               CAT(union_member)[T.Yes]  0.016   0.057   0.285      0.776   \n",
       "18          CAT(employment_type)[T.Other] -0.040   0.088  -0.449      0.653   \n",
       "19        CAT(employment_type)[T.Regular] -0.040   0.039  -1.032      0.302   \n",
       "20  CAT(employment_type)[T.Self-employed] -0.082   0.063  -1.310       0.19   \n",
       "21                           C_constraint  0.097   0.051   1.903      0.057   \n",
       "22                              S_support  0.711   0.051  13.987   1.87e-44   \n",
       "23                 C_constraint:S_support  0.054   0.071   0.760      0.447   \n",
       "24                                    age -0.001   0.002  -0.265      0.791   \n",
       "25                      ideology_1_7_last  0.024   0.017   1.422      0.155   \n",
       "\n",
       "    sig  ci_low  ci_high  \n",
       "0   ***   3.859    4.315  \n",
       "1        -0.070    0.069  \n",
       "2        -0.128    0.011  \n",
       "3     *  -0.008    0.172  \n",
       "4     *  -0.252    0.015  \n",
       "5        -0.040    0.164  \n",
       "6        -0.114    0.208  \n",
       "7        -0.170    0.111  \n",
       "8        -0.136    0.139  \n",
       "9        -0.166    0.126  \n",
       "10       -0.236    0.058  \n",
       "11       -0.218    0.073  \n",
       "12       -0.255    0.048  \n",
       "13       -0.147    0.175  \n",
       "14       -0.031    0.120  \n",
       "15       -0.157    0.033  \n",
       "16       -0.157    0.014  \n",
       "17       -0.095    0.128  \n",
       "18       -0.212    0.133  \n",
       "19       -0.117    0.036  \n",
       "20       -0.205    0.041  \n",
       "21    *  -0.003    0.196  \n",
       "22  ***   0.611    0.811  \n",
       "23       -0.085    0.193  \n",
       "24       -0.005    0.004  \n",
       "25       -0.009    0.057  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Spec B (Norm moderation): + Norm + Constraint×Norm**  \n",
       "N = 2,000 | R² = 0.204 | Adj. R² = 0.193"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "*Key terms:*"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>C_constraint</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.051</td>\n",
       "      <td>1.946</td>\n",
       "      <td>0.0516</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>S_support</td>\n",
       "      <td>0.711</td>\n",
       "      <td>0.051</td>\n",
       "      <td>13.966</td>\n",
       "      <td>2.52e-44</td>\n",
       "      <td>***</td>\n",
       "      <td>0.611</td>\n",
       "      <td>0.810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>C_constraint:S_support</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.719</td>\n",
       "      <td>0.472</td>\n",
       "      <td></td>\n",
       "      <td>-0.088</td>\n",
       "      <td>0.190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>C_constraint:norm_c</td>\n",
       "      <td>-0.094</td>\n",
       "      <td>0.032</td>\n",
       "      <td>-2.946</td>\n",
       "      <td>0.00322</td>\n",
       "      <td>***</td>\n",
       "      <td>-0.157</td>\n",
       "      <td>-0.032</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      term   coef  se_HC3       t         p  sig  ci_low  \\\n",
       "21            C_constraint  0.099   0.051   1.946    0.0516    *  -0.001   \n",
       "22               S_support  0.711   0.051  13.966  2.52e-44  ***   0.611   \n",
       "23  C_constraint:S_support  0.051   0.071   0.719     0.472       -0.088   \n",
       "25     C_constraint:norm_c -0.094   0.032  -2.946   0.00322  ***  -0.157   \n",
       "\n",
       "    ci_high  \n",
       "21    0.198  \n",
       "22    0.810  \n",
       "23    0.190  \n",
       "25   -0.032  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Full results — Spec B**  \n",
       "N = 2,000 | R² = 0.204 | Adj. R² = 0.193"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
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    },
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>4.138</td>\n",
       "      <td>0.117</td>\n",
       "      <td>35.280</td>\n",
       "      <td>1.17e-272</td>\n",
       "      <td>***</td>\n",
       "      <td>3.909</td>\n",
       "      <td>4.368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAT(gender)[T.Male]</td>\n",
       "      <td>-0.006</td>\n",
       "      <td>0.035</td>\n",
       "      <td>-0.182</td>\n",
       "      <td>0.855</td>\n",
       "      <td></td>\n",
       "      <td>-0.076</td>\n",
       "      <td>0.063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CAT(region)[T.SeoulMetro]</td>\n",
       "      <td>-0.060</td>\n",
       "      <td>0.035</td>\n",
       "      <td>-1.706</td>\n",
       "      <td>0.0881</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.130</td>\n",
       "      <td>0.009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAT(education)[T.College2yr]</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.046</td>\n",
       "      <td>1.287</td>\n",
       "      <td>0.198</td>\n",
       "      <td></td>\n",
       "      <td>-0.031</td>\n",
       "      <td>0.151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAT(education)[T.Grad+]</td>\n",
       "      <td>-0.095</td>\n",
       "      <td>0.068</td>\n",
       "      <td>-1.401</td>\n",
       "      <td>0.161</td>\n",
       "      <td></td>\n",
       "      <td>-0.229</td>\n",
       "      <td>0.038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CAT(education)[T.HighSchool]</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.423</td>\n",
       "      <td>0.673</td>\n",
       "      <td></td>\n",
       "      <td>-0.084</td>\n",
       "      <td>0.130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CAT(education)[T.MiddleOrLess]</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.92</td>\n",
       "      <td></td>\n",
       "      <td>-0.158</td>\n",
       "      <td>0.175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q2]</td>\n",
       "      <td>-0.036</td>\n",
       "      <td>0.071</td>\n",
       "      <td>-0.509</td>\n",
       "      <td>0.611</td>\n",
       "      <td></td>\n",
       "      <td>-0.176</td>\n",
       "      <td>0.104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q3]</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.070</td>\n",
       "      <td>-0.007</td>\n",
       "      <td>0.994</td>\n",
       "      <td></td>\n",
       "      <td>-0.138</td>\n",
       "      <td>0.137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q4]</td>\n",
       "      <td>-0.022</td>\n",
       "      <td>0.074</td>\n",
       "      <td>-0.299</td>\n",
       "      <td>0.765</td>\n",
       "      <td></td>\n",
       "      <td>-0.168</td>\n",
       "      <td>0.123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q5]</td>\n",
       "      <td>-0.090</td>\n",
       "      <td>0.075</td>\n",
       "      <td>-1.204</td>\n",
       "      <td>0.229</td>\n",
       "      <td></td>\n",
       "      <td>-0.236</td>\n",
       "      <td>0.056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q6]</td>\n",
       "      <td>-0.079</td>\n",
       "      <td>0.074</td>\n",
       "      <td>-1.060</td>\n",
       "      <td>0.289</td>\n",
       "      <td></td>\n",
       "      <td>-0.224</td>\n",
       "      <td>0.067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q7]</td>\n",
       "      <td>-0.104</td>\n",
       "      <td>0.077</td>\n",
       "      <td>-1.337</td>\n",
       "      <td>0.181</td>\n",
       "      <td></td>\n",
       "      <td>-0.255</td>\n",
       "      <td>0.048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q8]</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.895</td>\n",
       "      <td></td>\n",
       "      <td>-0.149</td>\n",
       "      <td>0.171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CAT(homeowner)[T.1]</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.038</td>\n",
       "      <td>1.196</td>\n",
       "      <td>0.232</td>\n",
       "      <td></td>\n",
       "      <td>-0.029</td>\n",
       "      <td>0.120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CAT(marital_status)[T.Other]</td>\n",
       "      <td>-0.058</td>\n",
       "      <td>0.048</td>\n",
       "      <td>-1.202</td>\n",
       "      <td>0.23</td>\n",
       "      <td></td>\n",
       "      <td>-0.152</td>\n",
       "      <td>0.036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CAT(marital_status)[T.Single]</td>\n",
       "      <td>-0.075</td>\n",
       "      <td>0.043</td>\n",
       "      <td>-1.733</td>\n",
       "      <td>0.0831</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.160</td>\n",
       "      <td>0.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAT(union_member)[T.Yes]</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.298</td>\n",
       "      <td>0.766</td>\n",
       "      <td></td>\n",
       "      <td>-0.093</td>\n",
       "      <td>0.127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CAT(employment_type)[T.Other]</td>\n",
       "      <td>-0.029</td>\n",
       "      <td>0.089</td>\n",
       "      <td>-0.324</td>\n",
       "      <td>0.746</td>\n",
       "      <td></td>\n",
       "      <td>-0.203</td>\n",
       "      <td>0.145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CAT(employment_type)[T.Regular]</td>\n",
       "      <td>-0.044</td>\n",
       "      <td>0.039</td>\n",
       "      <td>-1.126</td>\n",
       "      <td>0.26</td>\n",
       "      <td></td>\n",
       "      <td>-0.120</td>\n",
       "      <td>0.032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CAT(employment_type)[T.Self-employed]</td>\n",
       "      <td>-0.086</td>\n",
       "      <td>0.063</td>\n",
       "      <td>-1.370</td>\n",
       "      <td>0.171</td>\n",
       "      <td></td>\n",
       "      <td>-0.208</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>C_constraint</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.051</td>\n",
       "      <td>1.946</td>\n",
       "      <td>0.0516</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>S_support</td>\n",
       "      <td>0.711</td>\n",
       "      <td>0.051</td>\n",
       "      <td>13.966</td>\n",
       "      <td>2.52e-44</td>\n",
       "      <td>***</td>\n",
       "      <td>0.611</td>\n",
       "      <td>0.810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>C_constraint:S_support</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.719</td>\n",
       "      <td>0.472</td>\n",
       "      <td></td>\n",
       "      <td>-0.088</td>\n",
       "      <td>0.190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>norm_c</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.170</td>\n",
       "      <td>0.865</td>\n",
       "      <td></td>\n",
       "      <td>-0.043</td>\n",
       "      <td>0.051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>C_constraint:norm_c</td>\n",
       "      <td>-0.094</td>\n",
       "      <td>0.032</td>\n",
       "      <td>-2.946</td>\n",
       "      <td>0.00322</td>\n",
       "      <td>***</td>\n",
       "      <td>-0.157</td>\n",
       "      <td>-0.032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>age</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.002</td>\n",
       "      <td>-0.473</td>\n",
       "      <td>0.637</td>\n",
       "      <td></td>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ideology_1_7_last</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.017</td>\n",
       "      <td>1.245</td>\n",
       "      <td>0.213</td>\n",
       "      <td></td>\n",
       "      <td>-0.012</td>\n",
       "      <td>0.054</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     term   coef  se_HC3       t          p  \\\n",
       "0                               Intercept  4.138   0.117  35.280  1.17e-272   \n",
       "1                     CAT(gender)[T.Male] -0.006   0.035  -0.182      0.855   \n",
       "2               CAT(region)[T.SeoulMetro] -0.060   0.035  -1.706     0.0881   \n",
       "3            CAT(education)[T.College2yr]  0.060   0.046   1.287      0.198   \n",
       "4                 CAT(education)[T.Grad+] -0.095   0.068  -1.401      0.161   \n",
       "5            CAT(education)[T.HighSchool]  0.023   0.055   0.423      0.673   \n",
       "6          CAT(education)[T.MiddleOrLess]  0.009   0.085   0.100       0.92   \n",
       "7          CAT(monthly_income_band)[T.Q2] -0.036   0.071  -0.509      0.611   \n",
       "8          CAT(monthly_income_band)[T.Q3] -0.001   0.070  -0.007      0.994   \n",
       "9          CAT(monthly_income_band)[T.Q4] -0.022   0.074  -0.299      0.765   \n",
       "10         CAT(monthly_income_band)[T.Q5] -0.090   0.075  -1.204      0.229   \n",
       "11         CAT(monthly_income_band)[T.Q6] -0.079   0.074  -1.060      0.289   \n",
       "12         CAT(monthly_income_band)[T.Q7] -0.104   0.077  -1.337      0.181   \n",
       "13         CAT(monthly_income_band)[T.Q8]  0.011   0.082   0.132      0.895   \n",
       "14                    CAT(homeowner)[T.1]  0.046   0.038   1.196      0.232   \n",
       "15           CAT(marital_status)[T.Other] -0.058   0.048  -1.202       0.23   \n",
       "16          CAT(marital_status)[T.Single] -0.075   0.043  -1.733     0.0831   \n",
       "17               CAT(union_member)[T.Yes]  0.017   0.056   0.298      0.766   \n",
       "18          CAT(employment_type)[T.Other] -0.029   0.089  -0.324      0.746   \n",
       "19        CAT(employment_type)[T.Regular] -0.044   0.039  -1.126       0.26   \n",
       "20  CAT(employment_type)[T.Self-employed] -0.086   0.063  -1.370      0.171   \n",
       "21                           C_constraint  0.099   0.051   1.946     0.0516   \n",
       "22                              S_support  0.711   0.051  13.966   2.52e-44   \n",
       "23                 C_constraint:S_support  0.051   0.071   0.719      0.472   \n",
       "24                                 norm_c  0.004   0.024   0.170      0.865   \n",
       "25                    C_constraint:norm_c -0.094   0.032  -2.946    0.00322   \n",
       "26                                    age -0.001   0.002  -0.473      0.637   \n",
       "27                      ideology_1_7_last  0.021   0.017   1.245      0.213   \n",
       "\n",
       "    sig  ci_low  ci_high  \n",
       "0   ***   3.909    4.368  \n",
       "1        -0.076    0.063  \n",
       "2     *  -0.130    0.009  \n",
       "3        -0.031    0.151  \n",
       "4        -0.229    0.038  \n",
       "5        -0.084    0.130  \n",
       "6        -0.158    0.175  \n",
       "7        -0.176    0.104  \n",
       "8        -0.138    0.137  \n",
       "9        -0.168    0.123  \n",
       "10       -0.236    0.056  \n",
       "11       -0.224    0.067  \n",
       "12       -0.255    0.048  \n",
       "13       -0.149    0.171  \n",
       "14       -0.029    0.120  \n",
       "15       -0.152    0.036  \n",
       "16    *  -0.160    0.010  \n",
       "17       -0.093    0.127  \n",
       "18       -0.203    0.145  \n",
       "19       -0.120    0.032  \n",
       "20       -0.208    0.037  \n",
       "21    *  -0.001    0.198  \n",
       "22  ***   0.611    0.810  \n",
       "23       -0.088    0.190  \n",
       "24       -0.043    0.051  \n",
       "25  ***  -0.157   -0.032  \n",
       "26       -0.005    0.003  \n",
       "27       -0.012    0.054  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Spec C (Structural moderation): + LowCarbonProx + Constraint×LowCarbonProx**  \n",
       "N = 2,000 | R² = 0.226 | Adj. R² = 0.215"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "*Key terms:*"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>C_constraint</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.050</td>\n",
       "      <td>1.764</td>\n",
       "      <td>0.0778</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.010</td>\n",
       "      <td>0.186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>S_support</td>\n",
       "      <td>0.710</td>\n",
       "      <td>0.051</td>\n",
       "      <td>13.966</td>\n",
       "      <td>2.52e-44</td>\n",
       "      <td>***</td>\n",
       "      <td>0.611</td>\n",
       "      <td>0.810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>C_constraint:S_support</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.070</td>\n",
       "      <td>1.079</td>\n",
       "      <td>0.281</td>\n",
       "      <td></td>\n",
       "      <td>-0.062</td>\n",
       "      <td>0.212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>C_constraint:struc_c</td>\n",
       "      <td>-1.123</td>\n",
       "      <td>0.175</td>\n",
       "      <td>-6.417</td>\n",
       "      <td>1.39e-10</td>\n",
       "      <td>***</td>\n",
       "      <td>-1.466</td>\n",
       "      <td>-0.780</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      term   coef  se_HC3       t         p  sig  ci_low  \\\n",
       "21            C_constraint  0.088   0.050   1.764    0.0778    *  -0.010   \n",
       "22               S_support  0.710   0.051  13.966  2.52e-44  ***   0.611   \n",
       "23  C_constraint:S_support  0.075   0.070   1.079     0.281       -0.062   \n",
       "25    C_constraint:struc_c -1.123   0.175  -6.417  1.39e-10  ***  -1.466   \n",
       "\n",
       "    ci_high  \n",
       "21    0.186  \n",
       "22    0.810  \n",
       "23    0.212  \n",
       "25   -0.780  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Full results — Spec C**  \n",
       "N = 2,000 | R² = 0.226 | Adj. R² = 0.215"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
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     "output_type": "display_data"
    },
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>4.099</td>\n",
       "      <td>0.114</td>\n",
       "      <td>35.962</td>\n",
       "      <td>3.24e-283</td>\n",
       "      <td>***</td>\n",
       "      <td>3.876</td>\n",
       "      <td>4.323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAT(gender)[T.Male]</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.898</td>\n",
       "      <td></td>\n",
       "      <td>-0.064</td>\n",
       "      <td>0.073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CAT(region)[T.SeoulMetro]</td>\n",
       "      <td>-0.065</td>\n",
       "      <td>0.035</td>\n",
       "      <td>-1.865</td>\n",
       "      <td>0.0621</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.134</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAT(education)[T.College2yr]</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.045</td>\n",
       "      <td>1.335</td>\n",
       "      <td>0.182</td>\n",
       "      <td></td>\n",
       "      <td>-0.028</td>\n",
       "      <td>0.148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAT(education)[T.Grad+]</td>\n",
       "      <td>-0.134</td>\n",
       "      <td>0.067</td>\n",
       "      <td>-1.992</td>\n",
       "      <td>0.0463</td>\n",
       "      <td>**</td>\n",
       "      <td>-0.265</td>\n",
       "      <td>-0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CAT(education)[T.HighSchool]</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.843</td>\n",
       "      <td>0.399</td>\n",
       "      <td></td>\n",
       "      <td>-0.058</td>\n",
       "      <td>0.144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CAT(education)[T.MiddleOrLess]</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.379</td>\n",
       "      <td>0.705</td>\n",
       "      <td></td>\n",
       "      <td>-0.128</td>\n",
       "      <td>0.189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q2]</td>\n",
       "      <td>-0.031</td>\n",
       "      <td>0.071</td>\n",
       "      <td>-0.437</td>\n",
       "      <td>0.662</td>\n",
       "      <td></td>\n",
       "      <td>-0.171</td>\n",
       "      <td>0.109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q3]</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>0.069</td>\n",
       "      <td>-0.132</td>\n",
       "      <td>0.895</td>\n",
       "      <td></td>\n",
       "      <td>-0.145</td>\n",
       "      <td>0.127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q4]</td>\n",
       "      <td>-0.025</td>\n",
       "      <td>0.074</td>\n",
       "      <td>-0.332</td>\n",
       "      <td>0.74</td>\n",
       "      <td></td>\n",
       "      <td>-0.169</td>\n",
       "      <td>0.120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q5]</td>\n",
       "      <td>-0.093</td>\n",
       "      <td>0.075</td>\n",
       "      <td>-1.241</td>\n",
       "      <td>0.215</td>\n",
       "      <td></td>\n",
       "      <td>-0.239</td>\n",
       "      <td>0.054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q6]</td>\n",
       "      <td>-0.080</td>\n",
       "      <td>0.074</td>\n",
       "      <td>-1.091</td>\n",
       "      <td>0.275</td>\n",
       "      <td></td>\n",
       "      <td>-0.225</td>\n",
       "      <td>0.064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q7]</td>\n",
       "      <td>-0.093</td>\n",
       "      <td>0.077</td>\n",
       "      <td>-1.214</td>\n",
       "      <td>0.225</td>\n",
       "      <td></td>\n",
       "      <td>-0.244</td>\n",
       "      <td>0.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q8]</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.983</td>\n",
       "      <td></td>\n",
       "      <td>-0.157</td>\n",
       "      <td>0.160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CAT(homeowner)[T.1]</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.038</td>\n",
       "      <td>1.160</td>\n",
       "      <td>0.246</td>\n",
       "      <td></td>\n",
       "      <td>-0.030</td>\n",
       "      <td>0.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CAT(marital_status)[T.Other]</td>\n",
       "      <td>-0.051</td>\n",
       "      <td>0.047</td>\n",
       "      <td>-1.084</td>\n",
       "      <td>0.278</td>\n",
       "      <td></td>\n",
       "      <td>-0.144</td>\n",
       "      <td>0.041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CAT(marital_status)[T.Single]</td>\n",
       "      <td>-0.070</td>\n",
       "      <td>0.043</td>\n",
       "      <td>-1.637</td>\n",
       "      <td>0.102</td>\n",
       "      <td></td>\n",
       "      <td>-0.154</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAT(union_member)[T.Yes]</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>0.057</td>\n",
       "      <td>-0.163</td>\n",
       "      <td>0.871</td>\n",
       "      <td></td>\n",
       "      <td>-0.120</td>\n",
       "      <td>0.102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CAT(employment_type)[T.Other]</td>\n",
       "      <td>-0.046</td>\n",
       "      <td>0.088</td>\n",
       "      <td>-0.525</td>\n",
       "      <td>0.6</td>\n",
       "      <td></td>\n",
       "      <td>-0.218</td>\n",
       "      <td>0.126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CAT(employment_type)[T.Regular]</td>\n",
       "      <td>-0.040</td>\n",
       "      <td>0.038</td>\n",
       "      <td>-1.049</td>\n",
       "      <td>0.294</td>\n",
       "      <td></td>\n",
       "      <td>-0.115</td>\n",
       "      <td>0.035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CAT(employment_type)[T.Self-employed]</td>\n",
       "      <td>-0.086</td>\n",
       "      <td>0.062</td>\n",
       "      <td>-1.398</td>\n",
       "      <td>0.162</td>\n",
       "      <td></td>\n",
       "      <td>-0.207</td>\n",
       "      <td>0.035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>C_constraint</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.050</td>\n",
       "      <td>1.764</td>\n",
       "      <td>0.0778</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.010</td>\n",
       "      <td>0.186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>S_support</td>\n",
       "      <td>0.710</td>\n",
       "      <td>0.051</td>\n",
       "      <td>13.966</td>\n",
       "      <td>2.52e-44</td>\n",
       "      <td>***</td>\n",
       "      <td>0.611</td>\n",
       "      <td>0.810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>C_constraint:S_support</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.070</td>\n",
       "      <td>1.079</td>\n",
       "      <td>0.281</td>\n",
       "      <td></td>\n",
       "      <td>-0.062</td>\n",
       "      <td>0.212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>struc_c</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.485</td>\n",
       "      <td>0.628</td>\n",
       "      <td></td>\n",
       "      <td>-0.186</td>\n",
       "      <td>0.308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>C_constraint:struc_c</td>\n",
       "      <td>-1.123</td>\n",
       "      <td>0.175</td>\n",
       "      <td>-6.417</td>\n",
       "      <td>1.39e-10</td>\n",
       "      <td>***</td>\n",
       "      <td>-1.466</td>\n",
       "      <td>-0.780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>age</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>-0.119</td>\n",
       "      <td>0.905</td>\n",
       "      <td></td>\n",
       "      <td>-0.004</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ideology_1_7_last</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.017</td>\n",
       "      <td>1.324</td>\n",
       "      <td>0.186</td>\n",
       "      <td></td>\n",
       "      <td>-0.011</td>\n",
       "      <td>0.055</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     term   coef  se_HC3       t          p  \\\n",
       "0                               Intercept  4.099   0.114  35.962  3.24e-283   \n",
       "1                     CAT(gender)[T.Male]  0.004   0.035   0.128      0.898   \n",
       "2               CAT(region)[T.SeoulMetro] -0.065   0.035  -1.865     0.0621   \n",
       "3            CAT(education)[T.College2yr]  0.060   0.045   1.335      0.182   \n",
       "4                 CAT(education)[T.Grad+] -0.134   0.067  -1.992     0.0463   \n",
       "5            CAT(education)[T.HighSchool]  0.043   0.052   0.843      0.399   \n",
       "6          CAT(education)[T.MiddleOrLess]  0.031   0.081   0.379      0.705   \n",
       "7          CAT(monthly_income_band)[T.Q2] -0.031   0.071  -0.437      0.662   \n",
       "8          CAT(monthly_income_band)[T.Q3] -0.009   0.069  -0.132      0.895   \n",
       "9          CAT(monthly_income_band)[T.Q4] -0.025   0.074  -0.332       0.74   \n",
       "10         CAT(monthly_income_band)[T.Q5] -0.093   0.075  -1.241      0.215   \n",
       "11         CAT(monthly_income_band)[T.Q6] -0.080   0.074  -1.091      0.275   \n",
       "12         CAT(monthly_income_band)[T.Q7] -0.093   0.077  -1.214      0.225   \n",
       "13         CAT(monthly_income_band)[T.Q8]  0.002   0.081   0.021      0.983   \n",
       "14                    CAT(homeowner)[T.1]  0.044   0.038   1.160      0.246   \n",
       "15           CAT(marital_status)[T.Other] -0.051   0.047  -1.084      0.278   \n",
       "16          CAT(marital_status)[T.Single] -0.070   0.043  -1.637      0.102   \n",
       "17               CAT(union_member)[T.Yes] -0.009   0.057  -0.163      0.871   \n",
       "18          CAT(employment_type)[T.Other] -0.046   0.088  -0.525        0.6   \n",
       "19        CAT(employment_type)[T.Regular] -0.040   0.038  -1.049      0.294   \n",
       "20  CAT(employment_type)[T.Self-employed] -0.086   0.062  -1.398      0.162   \n",
       "21                           C_constraint  0.088   0.050   1.764     0.0778   \n",
       "22                              S_support  0.710   0.051  13.966   2.52e-44   \n",
       "23                 C_constraint:S_support  0.075   0.070   1.079      0.281   \n",
       "24                                struc_c  0.061   0.126   0.485      0.628   \n",
       "25                   C_constraint:struc_c -1.123   0.175  -6.417   1.39e-10   \n",
       "26                                    age -0.000   0.002  -0.119      0.905   \n",
       "27                      ideology_1_7_last  0.022   0.017   1.324      0.186   \n",
       "\n",
       "    sig  ci_low  ci_high  \n",
       "0   ***   3.876    4.323  \n",
       "1        -0.064    0.073  \n",
       "2     *  -0.134    0.003  \n",
       "3        -0.028    0.148  \n",
       "4    **  -0.265   -0.002  \n",
       "5        -0.058    0.144  \n",
       "6        -0.128    0.189  \n",
       "7        -0.171    0.109  \n",
       "8        -0.145    0.127  \n",
       "9        -0.169    0.120  \n",
       "10       -0.239    0.054  \n",
       "11       -0.225    0.064  \n",
       "12       -0.244    0.057  \n",
       "13       -0.157    0.160  \n",
       "14       -0.030    0.118  \n",
       "15       -0.144    0.041  \n",
       "16       -0.154    0.014  \n",
       "17       -0.120    0.102  \n",
       "18       -0.218    0.126  \n",
       "19       -0.115    0.035  \n",
       "20       -0.207    0.035  \n",
       "21    *  -0.010    0.186  \n",
       "22  ***   0.611    0.810  \n",
       "23       -0.062    0.212  \n",
       "24       -0.186    0.308  \n",
       "25  ***  -1.466   -0.780  \n",
       "26       -0.004    0.004  \n",
       "27       -0.011    0.055  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## Figure 3 (Transition support) — **H3 (Transition support)**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Spec A (baseline): DV ~ Constraint*Support + controls**  \n",
       "N = 2,000 | R² = 0.195 | Adj. R² = 0.185"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "*Key terms:*"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>C_constraint</td>\n",
       "      <td>0.899</td>\n",
       "      <td>0.061</td>\n",
       "      <td>14.651</td>\n",
       "      <td>1.33e-48</td>\n",
       "      <td>***</td>\n",
       "      <td>0.779</td>\n",
       "      <td>1.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>S_support</td>\n",
       "      <td>-0.300</td>\n",
       "      <td>0.056</td>\n",
       "      <td>-5.333</td>\n",
       "      <td>9.65e-08</td>\n",
       "      <td>***</td>\n",
       "      <td>-0.410</td>\n",
       "      <td>-0.190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>C_constraint:S_support</td>\n",
       "      <td>-0.130</td>\n",
       "      <td>0.087</td>\n",
       "      <td>-1.490</td>\n",
       "      <td>0.136</td>\n",
       "      <td></td>\n",
       "      <td>-0.302</td>\n",
       "      <td>0.041</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      term   coef  se_HC3       t         p  sig  ci_low  \\\n",
       "21            C_constraint  0.899   0.061  14.651  1.33e-48  ***   0.779   \n",
       "22               S_support -0.300   0.056  -5.333  9.65e-08  ***  -0.410   \n",
       "23  C_constraint:S_support -0.130   0.087  -1.490     0.136       -0.302   \n",
       "\n",
       "    ci_high  \n",
       "21    1.020  \n",
       "22   -0.190  \n",
       "23    0.041  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Full results — Spec A**  \n",
       "N = 2,000 | R² = 0.195 | Adj. R² = 0.185"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
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    },
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>3.874</td>\n",
       "      <td>0.143</td>\n",
       "      <td>27.030</td>\n",
       "      <td>6.61e-161</td>\n",
       "      <td>***</td>\n",
       "      <td>3.593</td>\n",
       "      <td>4.155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAT(gender)[T.Male]</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.251</td>\n",
       "      <td>0.802</td>\n",
       "      <td></td>\n",
       "      <td>-0.074</td>\n",
       "      <td>0.096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CAT(region)[T.SeoulMetro]</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0.044</td>\n",
       "      <td>1.992</td>\n",
       "      <td>0.0464</td>\n",
       "      <td>**</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAT(education)[T.College2yr]</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.058</td>\n",
       "      <td>1.226</td>\n",
       "      <td>0.22</td>\n",
       "      <td></td>\n",
       "      <td>-0.042</td>\n",
       "      <td>0.184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAT(education)[T.Grad+]</td>\n",
       "      <td>-0.083</td>\n",
       "      <td>0.079</td>\n",
       "      <td>-1.057</td>\n",
       "      <td>0.29</td>\n",
       "      <td></td>\n",
       "      <td>-0.238</td>\n",
       "      <td>0.071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CAT(education)[T.HighSchool]</td>\n",
       "      <td>0.208</td>\n",
       "      <td>0.065</td>\n",
       "      <td>3.208</td>\n",
       "      <td>0.00134</td>\n",
       "      <td>***</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CAT(education)[T.MiddleOrLess]</td>\n",
       "      <td>0.230</td>\n",
       "      <td>0.109</td>\n",
       "      <td>2.104</td>\n",
       "      <td>0.0354</td>\n",
       "      <td>**</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q2]</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.708</td>\n",
       "      <td>0.479</td>\n",
       "      <td></td>\n",
       "      <td>-0.107</td>\n",
       "      <td>0.228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q3]</td>\n",
       "      <td>-0.030</td>\n",
       "      <td>0.089</td>\n",
       "      <td>-0.336</td>\n",
       "      <td>0.737</td>\n",
       "      <td></td>\n",
       "      <td>-0.205</td>\n",
       "      <td>0.145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q4]</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.401</td>\n",
       "      <td>0.689</td>\n",
       "      <td></td>\n",
       "      <td>-0.141</td>\n",
       "      <td>0.213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q5]</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.091</td>\n",
       "      <td>0.164</td>\n",
       "      <td>0.87</td>\n",
       "      <td></td>\n",
       "      <td>-0.164</td>\n",
       "      <td>0.193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q6]</td>\n",
       "      <td>-0.050</td>\n",
       "      <td>0.092</td>\n",
       "      <td>-0.544</td>\n",
       "      <td>0.587</td>\n",
       "      <td></td>\n",
       "      <td>-0.230</td>\n",
       "      <td>0.130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q7]</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.993</td>\n",
       "      <td></td>\n",
       "      <td>-0.184</td>\n",
       "      <td>0.185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q8]</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.096</td>\n",
       "      <td>0.305</td>\n",
       "      <td>0.761</td>\n",
       "      <td></td>\n",
       "      <td>-0.160</td>\n",
       "      <td>0.218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CAT(homeowner)[T.1]</td>\n",
       "      <td>-0.055</td>\n",
       "      <td>0.047</td>\n",
       "      <td>-1.171</td>\n",
       "      <td>0.241</td>\n",
       "      <td></td>\n",
       "      <td>-0.146</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CAT(marital_status)[T.Other]</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.251</td>\n",
       "      <td>0.802</td>\n",
       "      <td></td>\n",
       "      <td>-0.105</td>\n",
       "      <td>0.136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CAT(marital_status)[T.Single]</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.705</td>\n",
       "      <td>0.48</td>\n",
       "      <td></td>\n",
       "      <td>-0.067</td>\n",
       "      <td>0.142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAT(union_member)[T.Yes]</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.071</td>\n",
       "      <td>1.118</td>\n",
       "      <td>0.264</td>\n",
       "      <td></td>\n",
       "      <td>-0.059</td>\n",
       "      <td>0.217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CAT(employment_type)[T.Other]</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.096</td>\n",
       "      <td>0.924</td>\n",
       "      <td></td>\n",
       "      <td>-0.186</td>\n",
       "      <td>0.205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CAT(employment_type)[T.Regular]</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.048</td>\n",
       "      <td>1.375</td>\n",
       "      <td>0.169</td>\n",
       "      <td></td>\n",
       "      <td>-0.028</td>\n",
       "      <td>0.161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CAT(employment_type)[T.Self-employed]</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.078</td>\n",
       "      <td>1.169</td>\n",
       "      <td>0.242</td>\n",
       "      <td></td>\n",
       "      <td>-0.062</td>\n",
       "      <td>0.246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>C_constraint</td>\n",
       "      <td>0.899</td>\n",
       "      <td>0.061</td>\n",
       "      <td>14.651</td>\n",
       "      <td>1.33e-48</td>\n",
       "      <td>***</td>\n",
       "      <td>0.779</td>\n",
       "      <td>1.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>S_support</td>\n",
       "      <td>-0.300</td>\n",
       "      <td>0.056</td>\n",
       "      <td>-5.333</td>\n",
       "      <td>9.65e-08</td>\n",
       "      <td>***</td>\n",
       "      <td>-0.410</td>\n",
       "      <td>-0.190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>C_constraint:S_support</td>\n",
       "      <td>-0.130</td>\n",
       "      <td>0.087</td>\n",
       "      <td>-1.490</td>\n",
       "      <td>0.136</td>\n",
       "      <td></td>\n",
       "      <td>-0.302</td>\n",
       "      <td>0.041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>age</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>0.003</td>\n",
       "      <td>-1.449</td>\n",
       "      <td>0.147</td>\n",
       "      <td></td>\n",
       "      <td>-0.009</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ideology_1_7_last</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.021</td>\n",
       "      <td>1.260</td>\n",
       "      <td>0.208</td>\n",
       "      <td></td>\n",
       "      <td>-0.015</td>\n",
       "      <td>0.067</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     term   coef  se_HC3       t          p  \\\n",
       "0                               Intercept  3.874   0.143  27.030  6.61e-161   \n",
       "1                     CAT(gender)[T.Male]  0.011   0.044   0.251      0.802   \n",
       "2               CAT(region)[T.SeoulMetro]  0.087   0.044   1.992     0.0464   \n",
       "3            CAT(education)[T.College2yr]  0.071   0.058   1.226       0.22   \n",
       "4                 CAT(education)[T.Grad+] -0.083   0.079  -1.057       0.29   \n",
       "5            CAT(education)[T.HighSchool]  0.208   0.065   3.208    0.00134   \n",
       "6          CAT(education)[T.MiddleOrLess]  0.230   0.109   2.104     0.0354   \n",
       "7          CAT(monthly_income_band)[T.Q2]  0.060   0.085   0.708      0.479   \n",
       "8          CAT(monthly_income_band)[T.Q3] -0.030   0.089  -0.336      0.737   \n",
       "9          CAT(monthly_income_band)[T.Q4]  0.036   0.090   0.401      0.689   \n",
       "10         CAT(monthly_income_band)[T.Q5]  0.015   0.091   0.164       0.87   \n",
       "11         CAT(monthly_income_band)[T.Q6] -0.050   0.092  -0.544      0.587   \n",
       "12         CAT(monthly_income_band)[T.Q7]  0.001   0.094   0.009      0.993   \n",
       "13         CAT(monthly_income_band)[T.Q8]  0.029   0.096   0.305      0.761   \n",
       "14                    CAT(homeowner)[T.1] -0.055   0.047  -1.171      0.241   \n",
       "15           CAT(marital_status)[T.Other]  0.015   0.062   0.251      0.802   \n",
       "16          CAT(marital_status)[T.Single]  0.038   0.053   0.705       0.48   \n",
       "17               CAT(union_member)[T.Yes]  0.079   0.071   1.118      0.264   \n",
       "18          CAT(employment_type)[T.Other]  0.010   0.100   0.096      0.924   \n",
       "19        CAT(employment_type)[T.Regular]  0.066   0.048   1.375      0.169   \n",
       "20  CAT(employment_type)[T.Self-employed]  0.092   0.078   1.169      0.242   \n",
       "21                           C_constraint  0.899   0.061  14.651   1.33e-48   \n",
       "22                              S_support -0.300   0.056  -5.333   9.65e-08   \n",
       "23                 C_constraint:S_support -0.130   0.087  -1.490      0.136   \n",
       "24                                    age -0.004   0.003  -1.449      0.147   \n",
       "25                      ideology_1_7_last  0.026   0.021   1.260      0.208   \n",
       "\n",
       "    sig  ci_low  ci_high  \n",
       "0   ***   3.593    4.155  \n",
       "1        -0.074    0.096  \n",
       "2    **   0.001    0.173  \n",
       "3        -0.042    0.184  \n",
       "4        -0.238    0.071  \n",
       "5   ***   0.081    0.335  \n",
       "6    **   0.016    0.443  \n",
       "7        -0.107    0.228  \n",
       "8        -0.205    0.145  \n",
       "9        -0.141    0.213  \n",
       "10       -0.164    0.193  \n",
       "11       -0.230    0.130  \n",
       "12       -0.184    0.185  \n",
       "13       -0.160    0.218  \n",
       "14       -0.146    0.037  \n",
       "15       -0.105    0.136  \n",
       "16       -0.067    0.142  \n",
       "17       -0.059    0.217  \n",
       "18       -0.186    0.205  \n",
       "19       -0.028    0.161  \n",
       "20       -0.062    0.246  \n",
       "21  ***   0.779    1.020  \n",
       "22  ***  -0.410   -0.190  \n",
       "23       -0.302    0.041  \n",
       "24       -0.009    0.001  \n",
       "25       -0.015    0.067  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Spec B (Norm moderation): + Norm + Constraint×Norm**  \n",
       "N = 2,000 | R² = 0.235 | Adj. R² = 0.225"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "*Key terms:*"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>C_constraint</td>\n",
       "      <td>0.906</td>\n",
       "      <td>0.059</td>\n",
       "      <td>15.247</td>\n",
       "      <td>1.73e-52</td>\n",
       "      <td>***</td>\n",
       "      <td>0.789</td>\n",
       "      <td>1.022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>S_support</td>\n",
       "      <td>-0.302</td>\n",
       "      <td>0.056</td>\n",
       "      <td>-5.400</td>\n",
       "      <td>6.65e-08</td>\n",
       "      <td>***</td>\n",
       "      <td>-0.412</td>\n",
       "      <td>-0.192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>C_constraint:S_support</td>\n",
       "      <td>-0.139</td>\n",
       "      <td>0.085</td>\n",
       "      <td>-1.630</td>\n",
       "      <td>0.103</td>\n",
       "      <td></td>\n",
       "      <td>-0.306</td>\n",
       "      <td>0.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>C_constraint:norm_c</td>\n",
       "      <td>-0.281</td>\n",
       "      <td>0.038</td>\n",
       "      <td>-7.411</td>\n",
       "      <td>1.26e-13</td>\n",
       "      <td>***</td>\n",
       "      <td>-0.355</td>\n",
       "      <td>-0.206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      term   coef  se_HC3       t         p  sig  ci_low  \\\n",
       "21            C_constraint  0.906   0.059  15.247  1.73e-52  ***   0.789   \n",
       "22               S_support -0.302   0.056  -5.400  6.65e-08  ***  -0.412   \n",
       "23  C_constraint:S_support -0.139   0.085  -1.630     0.103       -0.306   \n",
       "25     C_constraint:norm_c -0.281   0.038  -7.411  1.26e-13  ***  -0.355   \n",
       "\n",
       "    ci_high  \n",
       "21    1.022  \n",
       "22   -0.192  \n",
       "23    0.028  \n",
       "25   -0.206  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Full results — Spec B**  \n",
       "N = 2,000 | R² = 0.235 | Adj. R² = 0.225"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
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    },
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>4.053</td>\n",
       "      <td>0.141</td>\n",
       "      <td>28.646</td>\n",
       "      <td>1.78e-180</td>\n",
       "      <td>***</td>\n",
       "      <td>3.775</td>\n",
       "      <td>4.330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAT(gender)[T.Male]</td>\n",
       "      <td>-0.008</td>\n",
       "      <td>0.043</td>\n",
       "      <td>-0.185</td>\n",
       "      <td>0.853</td>\n",
       "      <td></td>\n",
       "      <td>-0.092</td>\n",
       "      <td>0.076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CAT(region)[T.SeoulMetro]</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.043</td>\n",
       "      <td>1.869</td>\n",
       "      <td>0.0616</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>0.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAT(education)[T.College2yr]</td>\n",
       "      <td>-0.006</td>\n",
       "      <td>0.058</td>\n",
       "      <td>-0.100</td>\n",
       "      <td>0.92</td>\n",
       "      <td></td>\n",
       "      <td>-0.119</td>\n",
       "      <td>0.108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAT(education)[T.Grad+]</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.078</td>\n",
       "      <td>-0.062</td>\n",
       "      <td>0.95</td>\n",
       "      <td></td>\n",
       "      <td>-0.158</td>\n",
       "      <td>0.148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CAT(education)[T.HighSchool]</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.066</td>\n",
       "      <td>1.067</td>\n",
       "      <td>0.286</td>\n",
       "      <td></td>\n",
       "      <td>-0.059</td>\n",
       "      <td>0.201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CAT(education)[T.MiddleOrLess]</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.826</td>\n",
       "      <td>0.409</td>\n",
       "      <td></td>\n",
       "      <td>-0.121</td>\n",
       "      <td>0.298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q2]</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.084</td>\n",
       "      <td>0.444</td>\n",
       "      <td>0.657</td>\n",
       "      <td></td>\n",
       "      <td>-0.127</td>\n",
       "      <td>0.201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q3]</td>\n",
       "      <td>-0.037</td>\n",
       "      <td>0.086</td>\n",
       "      <td>-0.427</td>\n",
       "      <td>0.669</td>\n",
       "      <td></td>\n",
       "      <td>-0.205</td>\n",
       "      <td>0.132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q4]</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.086</td>\n",
       "      <td>0.304</td>\n",
       "      <td>0.761</td>\n",
       "      <td></td>\n",
       "      <td>-0.143</td>\n",
       "      <td>0.195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q5]</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.902</td>\n",
       "      <td></td>\n",
       "      <td>-0.162</td>\n",
       "      <td>0.183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q6]</td>\n",
       "      <td>-0.070</td>\n",
       "      <td>0.090</td>\n",
       "      <td>-0.782</td>\n",
       "      <td>0.434</td>\n",
       "      <td></td>\n",
       "      <td>-0.246</td>\n",
       "      <td>0.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q7]</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.091</td>\n",
       "      <td>-0.014</td>\n",
       "      <td>0.989</td>\n",
       "      <td></td>\n",
       "      <td>-0.179</td>\n",
       "      <td>0.176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q8]</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.201</td>\n",
       "      <td>0.841</td>\n",
       "      <td></td>\n",
       "      <td>-0.165</td>\n",
       "      <td>0.202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CAT(homeowner)[T.1]</td>\n",
       "      <td>-0.051</td>\n",
       "      <td>0.045</td>\n",
       "      <td>-1.130</td>\n",
       "      <td>0.258</td>\n",
       "      <td></td>\n",
       "      <td>-0.140</td>\n",
       "      <td>0.038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CAT(marital_status)[T.Other]</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.466</td>\n",
       "      <td>0.641</td>\n",
       "      <td></td>\n",
       "      <td>-0.089</td>\n",
       "      <td>0.145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CAT(marital_status)[T.Single]</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.457</td>\n",
       "      <td>0.648</td>\n",
       "      <td></td>\n",
       "      <td>-0.078</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAT(union_member)[T.Yes]</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.070</td>\n",
       "      <td>1.155</td>\n",
       "      <td>0.248</td>\n",
       "      <td></td>\n",
       "      <td>-0.056</td>\n",
       "      <td>0.218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CAT(employment_type)[T.Other]</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.433</td>\n",
       "      <td>0.665</td>\n",
       "      <td></td>\n",
       "      <td>-0.150</td>\n",
       "      <td>0.236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CAT(employment_type)[T.Regular]</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.047</td>\n",
       "      <td>1.176</td>\n",
       "      <td>0.24</td>\n",
       "      <td></td>\n",
       "      <td>-0.037</td>\n",
       "      <td>0.147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CAT(employment_type)[T.Self-employed]</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.318</td>\n",
       "      <td></td>\n",
       "      <td>-0.075</td>\n",
       "      <td>0.230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>C_constraint</td>\n",
       "      <td>0.906</td>\n",
       "      <td>0.059</td>\n",
       "      <td>15.247</td>\n",
       "      <td>1.73e-52</td>\n",
       "      <td>***</td>\n",
       "      <td>0.789</td>\n",
       "      <td>1.022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>S_support</td>\n",
       "      <td>-0.302</td>\n",
       "      <td>0.056</td>\n",
       "      <td>-5.400</td>\n",
       "      <td>6.65e-08</td>\n",
       "      <td>***</td>\n",
       "      <td>-0.412</td>\n",
       "      <td>-0.192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>C_constraint:S_support</td>\n",
       "      <td>-0.139</td>\n",
       "      <td>0.085</td>\n",
       "      <td>-1.630</td>\n",
       "      <td>0.103</td>\n",
       "      <td></td>\n",
       "      <td>-0.306</td>\n",
       "      <td>0.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>norm_c</td>\n",
       "      <td>-0.010</td>\n",
       "      <td>0.027</td>\n",
       "      <td>-0.383</td>\n",
       "      <td>0.701</td>\n",
       "      <td></td>\n",
       "      <td>-0.063</td>\n",
       "      <td>0.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>C_constraint:norm_c</td>\n",
       "      <td>-0.281</td>\n",
       "      <td>0.038</td>\n",
       "      <td>-7.411</td>\n",
       "      <td>1.26e-13</td>\n",
       "      <td>***</td>\n",
       "      <td>-0.355</td>\n",
       "      <td>-0.206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>age</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.003</td>\n",
       "      <td>-2.072</td>\n",
       "      <td>0.0382</td>\n",
       "      <td>**</td>\n",
       "      <td>-0.010</td>\n",
       "      <td>-0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ideology_1_7_last</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.795</td>\n",
       "      <td>0.427</td>\n",
       "      <td></td>\n",
       "      <td>-0.024</td>\n",
       "      <td>0.056</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     term   coef  se_HC3       t          p  \\\n",
       "0                               Intercept  4.053   0.141  28.646  1.78e-180   \n",
       "1                     CAT(gender)[T.Male] -0.008   0.043  -0.185      0.853   \n",
       "2               CAT(region)[T.SeoulMetro]  0.080   0.043   1.869     0.0616   \n",
       "3            CAT(education)[T.College2yr] -0.006   0.058  -0.100       0.92   \n",
       "4                 CAT(education)[T.Grad+] -0.005   0.078  -0.062       0.95   \n",
       "5            CAT(education)[T.HighSchool]  0.071   0.066   1.067      0.286   \n",
       "6          CAT(education)[T.MiddleOrLess]  0.088   0.107   0.826      0.409   \n",
       "7          CAT(monthly_income_band)[T.Q2]  0.037   0.084   0.444      0.657   \n",
       "8          CAT(monthly_income_band)[T.Q3] -0.037   0.086  -0.427      0.669   \n",
       "9          CAT(monthly_income_band)[T.Q4]  0.026   0.086   0.304      0.761   \n",
       "10         CAT(monthly_income_band)[T.Q5]  0.011   0.088   0.123      0.902   \n",
       "11         CAT(monthly_income_band)[T.Q6] -0.070   0.090  -0.782      0.434   \n",
       "12         CAT(monthly_income_band)[T.Q7] -0.001   0.091  -0.014      0.989   \n",
       "13         CAT(monthly_income_band)[T.Q8]  0.019   0.094   0.201      0.841   \n",
       "14                    CAT(homeowner)[T.1] -0.051   0.045  -1.130      0.258   \n",
       "15           CAT(marital_status)[T.Other]  0.028   0.060   0.466      0.641   \n",
       "16          CAT(marital_status)[T.Single]  0.024   0.052   0.457      0.648   \n",
       "17               CAT(union_member)[T.Yes]  0.081   0.070   1.155      0.248   \n",
       "18          CAT(employment_type)[T.Other]  0.043   0.099   0.433      0.665   \n",
       "19        CAT(employment_type)[T.Regular]  0.055   0.047   1.176       0.24   \n",
       "20  CAT(employment_type)[T.Self-employed]  0.077   0.078   0.999      0.318   \n",
       "21                           C_constraint  0.906   0.059  15.247   1.73e-52   \n",
       "22                              S_support -0.302   0.056  -5.400   6.65e-08   \n",
       "23                 C_constraint:S_support -0.139   0.085  -1.630      0.103   \n",
       "24                                 norm_c -0.010   0.027  -0.383      0.701   \n",
       "25                    C_constraint:norm_c -0.281   0.038  -7.411   1.26e-13   \n",
       "26                                    age -0.005   0.003  -2.072     0.0382   \n",
       "27                      ideology_1_7_last  0.016   0.020   0.795      0.427   \n",
       "\n",
       "    sig  ci_low  ci_high  \n",
       "0   ***   3.775    4.330  \n",
       "1        -0.092    0.076  \n",
       "2     *  -0.004    0.163  \n",
       "3        -0.119    0.108  \n",
       "4        -0.158    0.148  \n",
       "5        -0.059    0.201  \n",
       "6        -0.121    0.298  \n",
       "7        -0.127    0.201  \n",
       "8        -0.205    0.132  \n",
       "9        -0.143    0.195  \n",
       "10       -0.162    0.183  \n",
       "11       -0.246    0.106  \n",
       "12       -0.179    0.176  \n",
       "13       -0.165    0.202  \n",
       "14       -0.140    0.038  \n",
       "15       -0.089    0.145  \n",
       "16       -0.078    0.125  \n",
       "17       -0.056    0.218  \n",
       "18       -0.150    0.236  \n",
       "19       -0.037    0.147  \n",
       "20       -0.075    0.230  \n",
       "21  ***   0.789    1.022  \n",
       "22  ***  -0.412   -0.192  \n",
       "23       -0.306    0.028  \n",
       "24       -0.063    0.042  \n",
       "25  ***  -0.355   -0.206  \n",
       "26   **  -0.010   -0.000  \n",
       "27       -0.024    0.056  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Spec C (Structural moderation): + LowCarbonProx + Constraint×LowCarbonProx**  \n",
       "N = 2,000 | R² = 0.326 | Adj. R² = 0.317"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "*Key terms:*"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>C_constraint</td>\n",
       "      <td>0.877</td>\n",
       "      <td>0.056</td>\n",
       "      <td>15.555</td>\n",
       "      <td>1.47e-54</td>\n",
       "      <td>***</td>\n",
       "      <td>0.766</td>\n",
       "      <td>0.987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>S_support</td>\n",
       "      <td>-0.302</td>\n",
       "      <td>0.056</td>\n",
       "      <td>-5.374</td>\n",
       "      <td>7.7e-08</td>\n",
       "      <td>***</td>\n",
       "      <td>-0.412</td>\n",
       "      <td>-0.192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>C_constraint:S_support</td>\n",
       "      <td>-0.072</td>\n",
       "      <td>0.080</td>\n",
       "      <td>-0.899</td>\n",
       "      <td>0.368</td>\n",
       "      <td></td>\n",
       "      <td>-0.229</td>\n",
       "      <td>0.085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>C_constraint:struc_c</td>\n",
       "      <td>-2.910</td>\n",
       "      <td>0.200</td>\n",
       "      <td>-14.521</td>\n",
       "      <td>8.89e-48</td>\n",
       "      <td>***</td>\n",
       "      <td>-3.303</td>\n",
       "      <td>-2.517</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      term   coef  se_HC3       t         p  sig  ci_low  \\\n",
       "21            C_constraint  0.877   0.056  15.555  1.47e-54  ***   0.766   \n",
       "22               S_support -0.302   0.056  -5.374   7.7e-08  ***  -0.412   \n",
       "23  C_constraint:S_support -0.072   0.080  -0.899     0.368       -0.229   \n",
       "25    C_constraint:struc_c -2.910   0.200 -14.521  8.89e-48  ***  -3.303   \n",
       "\n",
       "    ci_high  \n",
       "21    0.987  \n",
       "22   -0.192  \n",
       "23    0.085  \n",
       "25   -2.517  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Full results — Spec C**  \n",
       "N = 2,000 | R² = 0.326 | Adj. R² = 0.317"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
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    },
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>3.907</td>\n",
       "      <td>0.133</td>\n",
       "      <td>29.279</td>\n",
       "      <td>1.93e-188</td>\n",
       "      <td>***</td>\n",
       "      <td>3.646</td>\n",
       "      <td>4.169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAT(gender)[T.Male]</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.614</td>\n",
       "      <td>0.539</td>\n",
       "      <td></td>\n",
       "      <td>-0.054</td>\n",
       "      <td>0.103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CAT(region)[T.SeoulMetro]</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.040</td>\n",
       "      <td>1.700</td>\n",
       "      <td>0.0891</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.010</td>\n",
       "      <td>0.147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAT(education)[T.College2yr]</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.205</td>\n",
       "      <td>0.837</td>\n",
       "      <td></td>\n",
       "      <td>-0.091</td>\n",
       "      <td>0.112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAT(education)[T.Grad+]</td>\n",
       "      <td>-0.124</td>\n",
       "      <td>0.073</td>\n",
       "      <td>-1.696</td>\n",
       "      <td>0.0898</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.268</td>\n",
       "      <td>0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CAT(education)[T.HighSchool]</td>\n",
       "      <td>0.157</td>\n",
       "      <td>0.060</td>\n",
       "      <td>2.622</td>\n",
       "      <td>0.00875</td>\n",
       "      <td>***</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CAT(education)[T.MiddleOrLess]</td>\n",
       "      <td>0.183</td>\n",
       "      <td>0.104</td>\n",
       "      <td>1.749</td>\n",
       "      <td>0.0803</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.022</td>\n",
       "      <td>0.388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q2]</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.473</td>\n",
       "      <td></td>\n",
       "      <td>-0.097</td>\n",
       "      <td>0.209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q3]</td>\n",
       "      <td>-0.060</td>\n",
       "      <td>0.084</td>\n",
       "      <td>-0.714</td>\n",
       "      <td>0.475</td>\n",
       "      <td></td>\n",
       "      <td>-0.226</td>\n",
       "      <td>0.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q4]</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.084</td>\n",
       "      <td>0.279</td>\n",
       "      <td>0.781</td>\n",
       "      <td></td>\n",
       "      <td>-0.141</td>\n",
       "      <td>0.187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q5]</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.951</td>\n",
       "      <td></td>\n",
       "      <td>-0.159</td>\n",
       "      <td>0.169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q6]</td>\n",
       "      <td>-0.070</td>\n",
       "      <td>0.086</td>\n",
       "      <td>-0.814</td>\n",
       "      <td>0.416</td>\n",
       "      <td></td>\n",
       "      <td>-0.238</td>\n",
       "      <td>0.098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q7]</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0.324</td>\n",
       "      <td>0.746</td>\n",
       "      <td></td>\n",
       "      <td>-0.142</td>\n",
       "      <td>0.199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q8]</td>\n",
       "      <td>-0.003</td>\n",
       "      <td>0.091</td>\n",
       "      <td>-0.031</td>\n",
       "      <td>0.975</td>\n",
       "      <td></td>\n",
       "      <td>-0.180</td>\n",
       "      <td>0.175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CAT(homeowner)[T.1]</td>\n",
       "      <td>-0.057</td>\n",
       "      <td>0.043</td>\n",
       "      <td>-1.329</td>\n",
       "      <td>0.184</td>\n",
       "      <td></td>\n",
       "      <td>-0.141</td>\n",
       "      <td>0.027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CAT(marital_status)[T.Other]</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.781</td>\n",
       "      <td>0.435</td>\n",
       "      <td></td>\n",
       "      <td>-0.065</td>\n",
       "      <td>0.152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CAT(marital_status)[T.Single]</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.817</td>\n",
       "      <td>0.414</td>\n",
       "      <td></td>\n",
       "      <td>-0.056</td>\n",
       "      <td>0.136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAT(union_member)[T.Yes]</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.868</td>\n",
       "      <td></td>\n",
       "      <td>-0.120</td>\n",
       "      <td>0.142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CAT(employment_type)[T.Other]</td>\n",
       "      <td>-0.007</td>\n",
       "      <td>0.098</td>\n",
       "      <td>-0.071</td>\n",
       "      <td>0.944</td>\n",
       "      <td></td>\n",
       "      <td>-0.200</td>\n",
       "      <td>0.186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CAT(employment_type)[T.Regular]</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.044</td>\n",
       "      <td>1.510</td>\n",
       "      <td>0.131</td>\n",
       "      <td></td>\n",
       "      <td>-0.020</td>\n",
       "      <td>0.154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CAT(employment_type)[T.Self-employed]</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.071</td>\n",
       "      <td>1.139</td>\n",
       "      <td>0.255</td>\n",
       "      <td></td>\n",
       "      <td>-0.059</td>\n",
       "      <td>0.221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>C_constraint</td>\n",
       "      <td>0.877</td>\n",
       "      <td>0.056</td>\n",
       "      <td>15.555</td>\n",
       "      <td>1.47e-54</td>\n",
       "      <td>***</td>\n",
       "      <td>0.766</td>\n",
       "      <td>0.987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>S_support</td>\n",
       "      <td>-0.302</td>\n",
       "      <td>0.056</td>\n",
       "      <td>-5.374</td>\n",
       "      <td>7.7e-08</td>\n",
       "      <td>***</td>\n",
       "      <td>-0.412</td>\n",
       "      <td>-0.192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>C_constraint:S_support</td>\n",
       "      <td>-0.072</td>\n",
       "      <td>0.080</td>\n",
       "      <td>-0.899</td>\n",
       "      <td>0.368</td>\n",
       "      <td></td>\n",
       "      <td>-0.229</td>\n",
       "      <td>0.085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>struc_c</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.140</td>\n",
       "      <td>0.447</td>\n",
       "      <td>0.655</td>\n",
       "      <td></td>\n",
       "      <td>-0.212</td>\n",
       "      <td>0.337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>C_constraint:struc_c</td>\n",
       "      <td>-2.910</td>\n",
       "      <td>0.200</td>\n",
       "      <td>-14.521</td>\n",
       "      <td>8.89e-48</td>\n",
       "      <td>***</td>\n",
       "      <td>-3.303</td>\n",
       "      <td>-2.517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>age</td>\n",
       "      <td>-0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>-1.244</td>\n",
       "      <td>0.213</td>\n",
       "      <td></td>\n",
       "      <td>-0.008</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ideology_1_7_last</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.019</td>\n",
       "      <td>1.175</td>\n",
       "      <td>0.24</td>\n",
       "      <td></td>\n",
       "      <td>-0.015</td>\n",
       "      <td>0.059</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     term   coef  se_HC3       t          p  \\\n",
       "0                               Intercept  3.907   0.133  29.279  1.93e-188   \n",
       "1                     CAT(gender)[T.Male]  0.025   0.040   0.614      0.539   \n",
       "2               CAT(region)[T.SeoulMetro]  0.068   0.040   1.700     0.0891   \n",
       "3            CAT(education)[T.College2yr]  0.011   0.052   0.205      0.837   \n",
       "4                 CAT(education)[T.Grad+] -0.124   0.073  -1.696     0.0898   \n",
       "5            CAT(education)[T.HighSchool]  0.157   0.060   2.622    0.00875   \n",
       "6          CAT(education)[T.MiddleOrLess]  0.183   0.104   1.749     0.0803   \n",
       "7          CAT(monthly_income_band)[T.Q2]  0.056   0.078   0.718      0.473   \n",
       "8          CAT(monthly_income_band)[T.Q3] -0.060   0.084  -0.714      0.475   \n",
       "9          CAT(monthly_income_band)[T.Q4]  0.023   0.084   0.279      0.781   \n",
       "10         CAT(monthly_income_band)[T.Q5]  0.005   0.083   0.061      0.951   \n",
       "11         CAT(monthly_income_band)[T.Q6] -0.070   0.086  -0.814      0.416   \n",
       "12         CAT(monthly_income_band)[T.Q7]  0.028   0.087   0.324      0.746   \n",
       "13         CAT(monthly_income_band)[T.Q8] -0.003   0.091  -0.031      0.975   \n",
       "14                    CAT(homeowner)[T.1] -0.057   0.043  -1.329      0.184   \n",
       "15           CAT(marital_status)[T.Other]  0.043   0.056   0.781      0.435   \n",
       "16          CAT(marital_status)[T.Single]  0.040   0.049   0.817      0.414   \n",
       "17               CAT(union_member)[T.Yes]  0.011   0.067   0.166      0.868   \n",
       "18          CAT(employment_type)[T.Other] -0.007   0.098  -0.071      0.944   \n",
       "19        CAT(employment_type)[T.Regular]  0.067   0.044   1.510      0.131   \n",
       "20  CAT(employment_type)[T.Self-employed]  0.081   0.071   1.139      0.255   \n",
       "21                           C_constraint  0.877   0.056  15.555   1.47e-54   \n",
       "22                              S_support -0.302   0.056  -5.374    7.7e-08   \n",
       "23                 C_constraint:S_support -0.072   0.080  -0.899      0.368   \n",
       "24                                struc_c  0.063   0.140   0.447      0.655   \n",
       "25                   C_constraint:struc_c -2.910   0.200 -14.521   8.89e-48   \n",
       "26                                    age -0.003   0.002  -1.244      0.213   \n",
       "27                      ideology_1_7_last  0.022   0.019   1.175       0.24   \n",
       "\n",
       "    sig  ci_low  ci_high  \n",
       "0   ***   3.646    4.169  \n",
       "1        -0.054    0.103  \n",
       "2     *  -0.010    0.147  \n",
       "3        -0.091    0.112  \n",
       "4     *  -0.268    0.019  \n",
       "5   ***   0.040    0.274  \n",
       "6     *  -0.022    0.388  \n",
       "7        -0.097    0.209  \n",
       "8        -0.226    0.105  \n",
       "9        -0.141    0.187  \n",
       "10       -0.159    0.169  \n",
       "11       -0.238    0.098  \n",
       "12       -0.142    0.199  \n",
       "13       -0.180    0.175  \n",
       "14       -0.141    0.027  \n",
       "15       -0.065    0.152  \n",
       "16       -0.056    0.136  \n",
       "17       -0.120    0.142  \n",
       "18       -0.200    0.186  \n",
       "19       -0.020    0.154  \n",
       "20       -0.059    0.221  \n",
       "21  ***   0.766    0.987  \n",
       "22  ***  -0.412   -0.192  \n",
       "23       -0.229    0.085  \n",
       "24       -0.212    0.337  \n",
       "25  ***  -3.303   -2.517  \n",
       "26       -0.008    0.002  \n",
       "27       -0.015    0.059  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## Figure 4 (Regulatory flexibility) — **H4 (Regulatory flexibility)**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Spec A (baseline): DV ~ Constraint*Support + controls**  \n",
       "N = 2,000 | R² = 0.049 | Adj. R² = 0.037"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "*Key terms:*"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>C_constraint</td>\n",
       "      <td>0.239</td>\n",
       "      <td>0.062</td>\n",
       "      <td>3.843</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>***</td>\n",
       "      <td>0.117</td>\n",
       "      <td>0.360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>S_support</td>\n",
       "      <td>-0.211</td>\n",
       "      <td>0.057</td>\n",
       "      <td>-3.685</td>\n",
       "      <td>0.000229</td>\n",
       "      <td>***</td>\n",
       "      <td>-0.323</td>\n",
       "      <td>-0.099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>C_constraint:S_support</td>\n",
       "      <td>0.074</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.396</td>\n",
       "      <td></td>\n",
       "      <td>-0.096</td>\n",
       "      <td>0.243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      term   coef  se_HC3      t         p  sig  ci_low  \\\n",
       "21            C_constraint  0.239   0.062  3.843  0.000121  ***   0.117   \n",
       "22               S_support -0.211   0.057 -3.685  0.000229  ***  -0.323   \n",
       "23  C_constraint:S_support  0.074   0.087  0.848     0.396       -0.096   \n",
       "\n",
       "    ci_high  \n",
       "21    0.360  \n",
       "22   -0.099  \n",
       "23    0.243  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Full results — Spec A**  \n",
       "N = 2,000 | R² = 0.049 | Adj. R² = 0.037"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
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    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>3.494</td>\n",
       "      <td>0.139</td>\n",
       "      <td>25.082</td>\n",
       "      <td>7.87e-139</td>\n",
       "      <td>***</td>\n",
       "      <td>3.221</td>\n",
       "      <td>3.768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAT(gender)[T.Male]</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.043</td>\n",
       "      <td>1.187</td>\n",
       "      <td>0.235</td>\n",
       "      <td></td>\n",
       "      <td>-0.033</td>\n",
       "      <td>0.135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CAT(region)[T.SeoulMetro]</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.043</td>\n",
       "      <td>1.948</td>\n",
       "      <td>0.0515</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAT(education)[T.College2yr]</td>\n",
       "      <td>0.149</td>\n",
       "      <td>0.056</td>\n",
       "      <td>2.637</td>\n",
       "      <td>0.00836</td>\n",
       "      <td>***</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAT(education)[T.Grad+]</td>\n",
       "      <td>-0.133</td>\n",
       "      <td>0.080</td>\n",
       "      <td>-1.662</td>\n",
       "      <td>0.0966</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.290</td>\n",
       "      <td>0.024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CAT(education)[T.HighSchool]</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.064</td>\n",
       "      <td>3.948</td>\n",
       "      <td>7.89e-05</td>\n",
       "      <td>***</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CAT(education)[T.MiddleOrLess]</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.637</td>\n",
       "      <td>0.524</td>\n",
       "      <td></td>\n",
       "      <td>-0.143</td>\n",
       "      <td>0.281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q2]</td>\n",
       "      <td>-0.046</td>\n",
       "      <td>0.085</td>\n",
       "      <td>-0.542</td>\n",
       "      <td>0.588</td>\n",
       "      <td></td>\n",
       "      <td>-0.212</td>\n",
       "      <td>0.120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q3]</td>\n",
       "      <td>-0.026</td>\n",
       "      <td>0.086</td>\n",
       "      <td>-0.303</td>\n",
       "      <td>0.762</td>\n",
       "      <td></td>\n",
       "      <td>-0.195</td>\n",
       "      <td>0.143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q4]</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.892</td>\n",
       "      <td></td>\n",
       "      <td>-0.161</td>\n",
       "      <td>0.185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q5]</td>\n",
       "      <td>-0.034</td>\n",
       "      <td>0.093</td>\n",
       "      <td>-0.364</td>\n",
       "      <td>0.716</td>\n",
       "      <td></td>\n",
       "      <td>-0.217</td>\n",
       "      <td>0.149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q6]</td>\n",
       "      <td>-0.040</td>\n",
       "      <td>0.087</td>\n",
       "      <td>-0.460</td>\n",
       "      <td>0.646</td>\n",
       "      <td></td>\n",
       "      <td>-0.210</td>\n",
       "      <td>0.130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q7]</td>\n",
       "      <td>-0.038</td>\n",
       "      <td>0.092</td>\n",
       "      <td>-0.410</td>\n",
       "      <td>0.682</td>\n",
       "      <td></td>\n",
       "      <td>-0.218</td>\n",
       "      <td>0.142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q8]</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.103</td>\n",
       "      <td>1.070</td>\n",
       "      <td>0.285</td>\n",
       "      <td></td>\n",
       "      <td>-0.091</td>\n",
       "      <td>0.311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CAT(homeowner)[T.1]</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.971</td>\n",
       "      <td></td>\n",
       "      <td>-0.087</td>\n",
       "      <td>0.090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CAT(marital_status)[T.Other]</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.389</td>\n",
       "      <td>0.697</td>\n",
       "      <td></td>\n",
       "      <td>-0.089</td>\n",
       "      <td>0.134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CAT(marital_status)[T.Single]</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.529</td>\n",
       "      <td>0.597</td>\n",
       "      <td></td>\n",
       "      <td>-0.075</td>\n",
       "      <td>0.130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAT(union_member)[T.Yes]</td>\n",
       "      <td>-0.020</td>\n",
       "      <td>0.071</td>\n",
       "      <td>-0.280</td>\n",
       "      <td>0.78</td>\n",
       "      <td></td>\n",
       "      <td>-0.159</td>\n",
       "      <td>0.119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CAT(employment_type)[T.Other]</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.110</td>\n",
       "      <td>1.157</td>\n",
       "      <td>0.247</td>\n",
       "      <td></td>\n",
       "      <td>-0.089</td>\n",
       "      <td>0.344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CAT(employment_type)[T.Regular]</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.048</td>\n",
       "      <td>1.251</td>\n",
       "      <td>0.211</td>\n",
       "      <td></td>\n",
       "      <td>-0.034</td>\n",
       "      <td>0.155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CAT(employment_type)[T.Self-employed]</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.936</td>\n",
       "      <td>0.349</td>\n",
       "      <td></td>\n",
       "      <td>-0.078</td>\n",
       "      <td>0.221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>C_constraint</td>\n",
       "      <td>0.239</td>\n",
       "      <td>0.062</td>\n",
       "      <td>3.843</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>***</td>\n",
       "      <td>0.117</td>\n",
       "      <td>0.360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>S_support</td>\n",
       "      <td>-0.211</td>\n",
       "      <td>0.057</td>\n",
       "      <td>-3.685</td>\n",
       "      <td>0.000229</td>\n",
       "      <td>***</td>\n",
       "      <td>-0.323</td>\n",
       "      <td>-0.099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>C_constraint:S_support</td>\n",
       "      <td>0.074</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.396</td>\n",
       "      <td></td>\n",
       "      <td>-0.096</td>\n",
       "      <td>0.243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>age</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.992</td>\n",
       "      <td>0.321</td>\n",
       "      <td></td>\n",
       "      <td>-0.003</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ideology_1_7_last</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.396</td>\n",
       "      <td>0.692</td>\n",
       "      <td></td>\n",
       "      <td>-0.033</td>\n",
       "      <td>0.049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     term   coef  se_HC3       t          p  \\\n",
       "0                               Intercept  3.494   0.139  25.082  7.87e-139   \n",
       "1                     CAT(gender)[T.Male]  0.051   0.043   1.187      0.235   \n",
       "2               CAT(region)[T.SeoulMetro]  0.085   0.043   1.948     0.0515   \n",
       "3            CAT(education)[T.College2yr]  0.149   0.056   2.637    0.00836   \n",
       "4                 CAT(education)[T.Grad+] -0.133   0.080  -1.662     0.0966   \n",
       "5            CAT(education)[T.HighSchool]  0.254   0.064   3.948   7.89e-05   \n",
       "6          CAT(education)[T.MiddleOrLess]  0.069   0.108   0.637      0.524   \n",
       "7          CAT(monthly_income_band)[T.Q2] -0.046   0.085  -0.542      0.588   \n",
       "8          CAT(monthly_income_band)[T.Q3] -0.026   0.086  -0.303      0.762   \n",
       "9          CAT(monthly_income_band)[T.Q4]  0.012   0.088   0.135      0.892   \n",
       "10         CAT(monthly_income_band)[T.Q5] -0.034   0.093  -0.364      0.716   \n",
       "11         CAT(monthly_income_band)[T.Q6] -0.040   0.087  -0.460      0.646   \n",
       "12         CAT(monthly_income_band)[T.Q7] -0.038   0.092  -0.410      0.682   \n",
       "13         CAT(monthly_income_band)[T.Q8]  0.110   0.103   1.070      0.285   \n",
       "14                    CAT(homeowner)[T.1]  0.002   0.045   0.036      0.971   \n",
       "15           CAT(marital_status)[T.Other]  0.022   0.057   0.389      0.697   \n",
       "16          CAT(marital_status)[T.Single]  0.028   0.052   0.529      0.597   \n",
       "17               CAT(union_member)[T.Yes] -0.020   0.071  -0.280       0.78   \n",
       "18          CAT(employment_type)[T.Other]  0.128   0.110   1.157      0.247   \n",
       "19        CAT(employment_type)[T.Regular]  0.060   0.048   1.251      0.211   \n",
       "20  CAT(employment_type)[T.Self-employed]  0.071   0.076   0.936      0.349   \n",
       "21                           C_constraint  0.239   0.062   3.843   0.000121   \n",
       "22                              S_support -0.211   0.057  -3.685   0.000229   \n",
       "23                 C_constraint:S_support  0.074   0.087   0.848      0.396   \n",
       "24                                    age  0.003   0.003   0.992      0.321   \n",
       "25                      ideology_1_7_last  0.008   0.021   0.396      0.692   \n",
       "\n",
       "    sig  ci_low  ci_high  \n",
       "0   ***   3.221    3.768  \n",
       "1        -0.033    0.135  \n",
       "2     *  -0.001    0.170  \n",
       "3   ***   0.038    0.259  \n",
       "4     *  -0.290    0.024  \n",
       "5   ***   0.128    0.381  \n",
       "6        -0.143    0.281  \n",
       "7        -0.212    0.120  \n",
       "8        -0.195    0.143  \n",
       "9        -0.161    0.185  \n",
       "10       -0.217    0.149  \n",
       "11       -0.210    0.130  \n",
       "12       -0.218    0.142  \n",
       "13       -0.091    0.311  \n",
       "14       -0.087    0.090  \n",
       "15       -0.089    0.134  \n",
       "16       -0.075    0.130  \n",
       "17       -0.159    0.119  \n",
       "18       -0.089    0.344  \n",
       "19       -0.034    0.155  \n",
       "20       -0.078    0.221  \n",
       "21  ***   0.117    0.360  \n",
       "22  ***  -0.323   -0.099  \n",
       "23       -0.096    0.243  \n",
       "24       -0.003    0.008  \n",
       "25       -0.033    0.049  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Spec B (Norm moderation): + Norm + Constraint×Norm**  \n",
       "N = 2,000 | R² = 0.148 | Adj. R² = 0.137"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "*Key terms:*"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>C_constraint</td>\n",
       "      <td>0.248</td>\n",
       "      <td>0.058</td>\n",
       "      <td>4.272</td>\n",
       "      <td>1.94e-05</td>\n",
       "      <td>***</td>\n",
       "      <td>0.134</td>\n",
       "      <td>0.362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>S_support</td>\n",
       "      <td>-0.212</td>\n",
       "      <td>0.057</td>\n",
       "      <td>-3.733</td>\n",
       "      <td>0.000189</td>\n",
       "      <td>***</td>\n",
       "      <td>-0.324</td>\n",
       "      <td>-0.101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>C_constraint:S_support</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.727</td>\n",
       "      <td>0.467</td>\n",
       "      <td></td>\n",
       "      <td>-0.101</td>\n",
       "      <td>0.221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>C_constraint:norm_c</td>\n",
       "      <td>-0.426</td>\n",
       "      <td>0.037</td>\n",
       "      <td>-11.672</td>\n",
       "      <td>1.77e-31</td>\n",
       "      <td>***</td>\n",
       "      <td>-0.498</td>\n",
       "      <td>-0.355</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      term   coef  se_HC3       t         p  sig  ci_low  \\\n",
       "21            C_constraint  0.248   0.058   4.272  1.94e-05  ***   0.134   \n",
       "22               S_support -0.212   0.057  -3.733  0.000189  ***  -0.324   \n",
       "23  C_constraint:S_support  0.060   0.082   0.727     0.467       -0.101   \n",
       "25     C_constraint:norm_c -0.426   0.037 -11.672  1.77e-31  ***  -0.498   \n",
       "\n",
       "    ci_high  \n",
       "21    0.362  \n",
       "22   -0.101  \n",
       "23    0.221  \n",
       "25   -0.355  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Full results — Spec B**  \n",
       "N = 2,000 | R² = 0.148 | Adj. R² = 0.137"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
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    },
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>3.728</td>\n",
       "      <td>0.137</td>\n",
       "      <td>27.307</td>\n",
       "      <td>3.55e-164</td>\n",
       "      <td>***</td>\n",
       "      <td>3.461</td>\n",
       "      <td>3.996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAT(gender)[T.Male]</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.617</td>\n",
       "      <td>0.538</td>\n",
       "      <td></td>\n",
       "      <td>-0.055</td>\n",
       "      <td>0.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CAT(region)[T.SeoulMetro]</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.041</td>\n",
       "      <td>1.828</td>\n",
       "      <td>0.0676</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAT(education)[T.College2yr]</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.870</td>\n",
       "      <td>0.384</td>\n",
       "      <td></td>\n",
       "      <td>-0.059</td>\n",
       "      <td>0.153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAT(education)[T.Grad+]</td>\n",
       "      <td>-0.027</td>\n",
       "      <td>0.074</td>\n",
       "      <td>-0.362</td>\n",
       "      <td>0.717</td>\n",
       "      <td></td>\n",
       "      <td>-0.173</td>\n",
       "      <td>0.119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CAT(education)[T.HighSchool]</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.064</td>\n",
       "      <td>1.179</td>\n",
       "      <td>0.238</td>\n",
       "      <td></td>\n",
       "      <td>-0.050</td>\n",
       "      <td>0.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CAT(education)[T.MiddleOrLess]</td>\n",
       "      <td>-0.107</td>\n",
       "      <td>0.106</td>\n",
       "      <td>-1.015</td>\n",
       "      <td>0.31</td>\n",
       "      <td></td>\n",
       "      <td>-0.314</td>\n",
       "      <td>0.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q2]</td>\n",
       "      <td>-0.077</td>\n",
       "      <td>0.081</td>\n",
       "      <td>-0.950</td>\n",
       "      <td>0.342</td>\n",
       "      <td></td>\n",
       "      <td>-0.235</td>\n",
       "      <td>0.082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q3]</td>\n",
       "      <td>-0.036</td>\n",
       "      <td>0.081</td>\n",
       "      <td>-0.441</td>\n",
       "      <td>0.66</td>\n",
       "      <td></td>\n",
       "      <td>-0.195</td>\n",
       "      <td>0.124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q4]</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.084</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.995</td>\n",
       "      <td></td>\n",
       "      <td>-0.163</td>\n",
       "      <td>0.164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q5]</td>\n",
       "      <td>-0.037</td>\n",
       "      <td>0.087</td>\n",
       "      <td>-0.429</td>\n",
       "      <td>0.668</td>\n",
       "      <td></td>\n",
       "      <td>-0.208</td>\n",
       "      <td>0.133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q6]</td>\n",
       "      <td>-0.067</td>\n",
       "      <td>0.083</td>\n",
       "      <td>-0.802</td>\n",
       "      <td>0.422</td>\n",
       "      <td></td>\n",
       "      <td>-0.230</td>\n",
       "      <td>0.096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q7]</td>\n",
       "      <td>-0.038</td>\n",
       "      <td>0.087</td>\n",
       "      <td>-0.434</td>\n",
       "      <td>0.664</td>\n",
       "      <td></td>\n",
       "      <td>-0.208</td>\n",
       "      <td>0.133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q8]</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.096</td>\n",
       "      <td>0.991</td>\n",
       "      <td>0.322</td>\n",
       "      <td></td>\n",
       "      <td>-0.093</td>\n",
       "      <td>0.283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CAT(homeowner)[T.1]</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.163</td>\n",
       "      <td>0.871</td>\n",
       "      <td></td>\n",
       "      <td>-0.077</td>\n",
       "      <td>0.091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CAT(marital_status)[T.Other]</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.757</td>\n",
       "      <td>0.449</td>\n",
       "      <td></td>\n",
       "      <td>-0.065</td>\n",
       "      <td>0.148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CAT(marital_status)[T.Single]</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.851</td>\n",
       "      <td></td>\n",
       "      <td>-0.088</td>\n",
       "      <td>0.107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAT(union_member)[T.Yes]</td>\n",
       "      <td>-0.017</td>\n",
       "      <td>0.064</td>\n",
       "      <td>-0.271</td>\n",
       "      <td>0.786</td>\n",
       "      <td></td>\n",
       "      <td>-0.142</td>\n",
       "      <td>0.108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CAT(employment_type)[T.Other]</td>\n",
       "      <td>0.177</td>\n",
       "      <td>0.105</td>\n",
       "      <td>1.691</td>\n",
       "      <td>0.0908</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.028</td>\n",
       "      <td>0.382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CAT(employment_type)[T.Regular]</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.971</td>\n",
       "      <td>0.332</td>\n",
       "      <td></td>\n",
       "      <td>-0.045</td>\n",
       "      <td>0.133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CAT(employment_type)[T.Self-employed]</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.072</td>\n",
       "      <td>0.763</td>\n",
       "      <td>0.445</td>\n",
       "      <td></td>\n",
       "      <td>-0.086</td>\n",
       "      <td>0.195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>C_constraint</td>\n",
       "      <td>0.248</td>\n",
       "      <td>0.058</td>\n",
       "      <td>4.272</td>\n",
       "      <td>1.94e-05</td>\n",
       "      <td>***</td>\n",
       "      <td>0.134</td>\n",
       "      <td>0.362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>S_support</td>\n",
       "      <td>-0.212</td>\n",
       "      <td>0.057</td>\n",
       "      <td>-3.733</td>\n",
       "      <td>0.000189</td>\n",
       "      <td>***</td>\n",
       "      <td>-0.324</td>\n",
       "      <td>-0.101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>C_constraint:S_support</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.727</td>\n",
       "      <td>0.467</td>\n",
       "      <td></td>\n",
       "      <td>-0.101</td>\n",
       "      <td>0.221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>norm_c</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.617</td>\n",
       "      <td>0.538</td>\n",
       "      <td></td>\n",
       "      <td>-0.036</td>\n",
       "      <td>0.068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>C_constraint:norm_c</td>\n",
       "      <td>-0.426</td>\n",
       "      <td>0.037</td>\n",
       "      <td>-11.672</td>\n",
       "      <td>1.77e-31</td>\n",
       "      <td>***</td>\n",
       "      <td>-0.498</td>\n",
       "      <td>-0.355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>age</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0.795</td>\n",
       "      <td></td>\n",
       "      <td>-0.004</td>\n",
       "      <td>0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ideology_1_7_last</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.020</td>\n",
       "      <td>-0.277</td>\n",
       "      <td>0.782</td>\n",
       "      <td></td>\n",
       "      <td>-0.044</td>\n",
       "      <td>0.033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     term   coef  se_HC3       t          p  \\\n",
       "0                               Intercept  3.728   0.137  27.307  3.55e-164   \n",
       "1                     CAT(gender)[T.Male]  0.025   0.041   0.617      0.538   \n",
       "2               CAT(region)[T.SeoulMetro]  0.075   0.041   1.828     0.0676   \n",
       "3            CAT(education)[T.College2yr]  0.047   0.054   0.870      0.384   \n",
       "4                 CAT(education)[T.Grad+] -0.027   0.074  -0.362      0.717   \n",
       "5            CAT(education)[T.HighSchool]  0.075   0.064   1.179      0.238   \n",
       "6          CAT(education)[T.MiddleOrLess] -0.107   0.106  -1.015       0.31   \n",
       "7          CAT(monthly_income_band)[T.Q2] -0.077   0.081  -0.950      0.342   \n",
       "8          CAT(monthly_income_band)[T.Q3] -0.036   0.081  -0.441       0.66   \n",
       "9          CAT(monthly_income_band)[T.Q4]  0.001   0.084   0.006      0.995   \n",
       "10         CAT(monthly_income_band)[T.Q5] -0.037   0.087  -0.429      0.668   \n",
       "11         CAT(monthly_income_band)[T.Q6] -0.067   0.083  -0.802      0.422   \n",
       "12         CAT(monthly_income_band)[T.Q7] -0.038   0.087  -0.434      0.664   \n",
       "13         CAT(monthly_income_band)[T.Q8]  0.095   0.096   0.991      0.322   \n",
       "14                    CAT(homeowner)[T.1]  0.007   0.043   0.163      0.871   \n",
       "15           CAT(marital_status)[T.Other]  0.041   0.054   0.757      0.449   \n",
       "16          CAT(marital_status)[T.Single]  0.009   0.050   0.188      0.851   \n",
       "17               CAT(union_member)[T.Yes] -0.017   0.064  -0.271      0.786   \n",
       "18          CAT(employment_type)[T.Other]  0.177   0.105   1.691     0.0908   \n",
       "19        CAT(employment_type)[T.Regular]  0.044   0.046   0.971      0.332   \n",
       "20  CAT(employment_type)[T.Self-employed]  0.055   0.072   0.763      0.445   \n",
       "21                           C_constraint  0.248   0.058   4.272   1.94e-05   \n",
       "22                              S_support -0.212   0.057  -3.733   0.000189   \n",
       "23                 C_constraint:S_support  0.060   0.082   0.727      0.467   \n",
       "24                                 norm_c  0.016   0.027   0.617      0.538   \n",
       "25                    C_constraint:norm_c -0.426   0.037 -11.672   1.77e-31   \n",
       "26                                    age  0.001   0.002   0.260      0.795   \n",
       "27                      ideology_1_7_last -0.005   0.020  -0.277      0.782   \n",
       "\n",
       "    sig  ci_low  ci_high  \n",
       "0   ***   3.461    3.996  \n",
       "1        -0.055    0.105  \n",
       "2     *  -0.005    0.156  \n",
       "3        -0.059    0.153  \n",
       "4        -0.173    0.119  \n",
       "5        -0.050    0.200  \n",
       "6        -0.314    0.100  \n",
       "7        -0.235    0.082  \n",
       "8        -0.195    0.124  \n",
       "9        -0.163    0.164  \n",
       "10       -0.208    0.133  \n",
       "11       -0.230    0.096  \n",
       "12       -0.208    0.133  \n",
       "13       -0.093    0.283  \n",
       "14       -0.077    0.091  \n",
       "15       -0.065    0.148  \n",
       "16       -0.088    0.107  \n",
       "17       -0.142    0.108  \n",
       "18    *  -0.028    0.382  \n",
       "19       -0.045    0.133  \n",
       "20       -0.086    0.195  \n",
       "21  ***   0.134    0.362  \n",
       "22  ***  -0.324   -0.101  \n",
       "23       -0.101    0.221  \n",
       "24       -0.036    0.068  \n",
       "25  ***  -0.498   -0.355  \n",
       "26       -0.004    0.005  \n",
       "27       -0.044    0.033  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Spec C (Structural moderation): + LowCarbonProx + Constraint×LowCarbonProx**  \n",
       "N = 2,000 | R² = 0.098 | Adj. R² = 0.085"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "*Key terms:*"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>C_constraint</td>\n",
       "      <td>0.226</td>\n",
       "      <td>0.061</td>\n",
       "      <td>3.723</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>***</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>S_support</td>\n",
       "      <td>-0.212</td>\n",
       "      <td>0.057</td>\n",
       "      <td>-3.704</td>\n",
       "      <td>0.000213</td>\n",
       "      <td>***</td>\n",
       "      <td>-0.324</td>\n",
       "      <td>-0.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>C_constraint:S_support</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.084</td>\n",
       "      <td>1.245</td>\n",
       "      <td>0.213</td>\n",
       "      <td></td>\n",
       "      <td>-0.060</td>\n",
       "      <td>0.271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>C_constraint:struc_c</td>\n",
       "      <td>-1.654</td>\n",
       "      <td>0.210</td>\n",
       "      <td>-7.874</td>\n",
       "      <td>3.44e-15</td>\n",
       "      <td>***</td>\n",
       "      <td>-2.066</td>\n",
       "      <td>-1.242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      term   coef  se_HC3      t         p  sig  ci_low  \\\n",
       "21            C_constraint  0.226   0.061  3.723  0.000197  ***   0.107   \n",
       "22               S_support -0.212   0.057 -3.704  0.000213  ***  -0.324   \n",
       "23  C_constraint:S_support  0.105   0.084  1.245     0.213       -0.060   \n",
       "25    C_constraint:struc_c -1.654   0.210 -7.874  3.44e-15  ***  -2.066   \n",
       "\n",
       "    ci_high  \n",
       "21    0.346  \n",
       "22   -0.100  \n",
       "23    0.271  \n",
       "25   -1.242  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Full results — Spec C**  \n",
       "N = 2,000 | R² = 0.098 | Adj. R² = 0.085"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
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    },
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>3.512</td>\n",
       "      <td>0.135</td>\n",
       "      <td>26.049</td>\n",
       "      <td>1.4e-149</td>\n",
       "      <td>***</td>\n",
       "      <td>3.248</td>\n",
       "      <td>3.777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAT(gender)[T.Male]</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.042</td>\n",
       "      <td>1.396</td>\n",
       "      <td>0.163</td>\n",
       "      <td></td>\n",
       "      <td>-0.024</td>\n",
       "      <td>0.141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CAT(region)[T.SeoulMetro]</td>\n",
       "      <td>0.074</td>\n",
       "      <td>0.042</td>\n",
       "      <td>1.756</td>\n",
       "      <td>0.0791</td>\n",
       "      <td>*</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>0.157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAT(education)[T.College2yr]</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.054</td>\n",
       "      <td>2.142</td>\n",
       "      <td>0.0322</td>\n",
       "      <td>**</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAT(education)[T.Grad+]</td>\n",
       "      <td>-0.155</td>\n",
       "      <td>0.079</td>\n",
       "      <td>-1.974</td>\n",
       "      <td>0.0484</td>\n",
       "      <td>**</td>\n",
       "      <td>-0.309</td>\n",
       "      <td>-0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CAT(education)[T.HighSchool]</td>\n",
       "      <td>0.226</td>\n",
       "      <td>0.063</td>\n",
       "      <td>3.601</td>\n",
       "      <td>0.000318</td>\n",
       "      <td>***</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CAT(education)[T.MiddleOrLess]</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.415</td>\n",
       "      <td>0.678</td>\n",
       "      <td></td>\n",
       "      <td>-0.166</td>\n",
       "      <td>0.256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q2]</td>\n",
       "      <td>-0.048</td>\n",
       "      <td>0.082</td>\n",
       "      <td>-0.586</td>\n",
       "      <td>0.558</td>\n",
       "      <td></td>\n",
       "      <td>-0.210</td>\n",
       "      <td>0.113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q3]</td>\n",
       "      <td>-0.042</td>\n",
       "      <td>0.085</td>\n",
       "      <td>-0.497</td>\n",
       "      <td>0.619</td>\n",
       "      <td></td>\n",
       "      <td>-0.208</td>\n",
       "      <td>0.124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q4]</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.955</td>\n",
       "      <td></td>\n",
       "      <td>-0.162</td>\n",
       "      <td>0.172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q5]</td>\n",
       "      <td>-0.039</td>\n",
       "      <td>0.091</td>\n",
       "      <td>-0.433</td>\n",
       "      <td>0.665</td>\n",
       "      <td></td>\n",
       "      <td>-0.217</td>\n",
       "      <td>0.139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q6]</td>\n",
       "      <td>-0.051</td>\n",
       "      <td>0.086</td>\n",
       "      <td>-0.592</td>\n",
       "      <td>0.554</td>\n",
       "      <td></td>\n",
       "      <td>-0.220</td>\n",
       "      <td>0.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q7]</td>\n",
       "      <td>-0.022</td>\n",
       "      <td>0.089</td>\n",
       "      <td>-0.251</td>\n",
       "      <td>0.802</td>\n",
       "      <td></td>\n",
       "      <td>-0.197</td>\n",
       "      <td>0.152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q8]</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.918</td>\n",
       "      <td>0.358</td>\n",
       "      <td></td>\n",
       "      <td>-0.104</td>\n",
       "      <td>0.287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CAT(homeowner)[T.1]</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.982</td>\n",
       "      <td></td>\n",
       "      <td>-0.085</td>\n",
       "      <td>0.087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CAT(marital_status)[T.Other]</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.679</td>\n",
       "      <td>0.497</td>\n",
       "      <td></td>\n",
       "      <td>-0.071</td>\n",
       "      <td>0.147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CAT(marital_status)[T.Single]</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.580</td>\n",
       "      <td>0.562</td>\n",
       "      <td></td>\n",
       "      <td>-0.070</td>\n",
       "      <td>0.129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAT(union_member)[T.Yes]</td>\n",
       "      <td>-0.057</td>\n",
       "      <td>0.070</td>\n",
       "      <td>-0.817</td>\n",
       "      <td>0.414</td>\n",
       "      <td></td>\n",
       "      <td>-0.195</td>\n",
       "      <td>0.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CAT(employment_type)[T.Other]</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.110</td>\n",
       "      <td>1.075</td>\n",
       "      <td>0.282</td>\n",
       "      <td></td>\n",
       "      <td>-0.097</td>\n",
       "      <td>0.334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CAT(employment_type)[T.Regular]</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.047</td>\n",
       "      <td>1.294</td>\n",
       "      <td>0.196</td>\n",
       "      <td></td>\n",
       "      <td>-0.031</td>\n",
       "      <td>0.152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CAT(employment_type)[T.Self-employed]</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.865</td>\n",
       "      <td>0.387</td>\n",
       "      <td></td>\n",
       "      <td>-0.083</td>\n",
       "      <td>0.214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>C_constraint</td>\n",
       "      <td>0.226</td>\n",
       "      <td>0.061</td>\n",
       "      <td>3.723</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>***</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>S_support</td>\n",
       "      <td>-0.212</td>\n",
       "      <td>0.057</td>\n",
       "      <td>-3.704</td>\n",
       "      <td>0.000213</td>\n",
       "      <td>***</td>\n",
       "      <td>-0.324</td>\n",
       "      <td>-0.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>C_constraint:S_support</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.084</td>\n",
       "      <td>1.245</td>\n",
       "      <td>0.213</td>\n",
       "      <td></td>\n",
       "      <td>-0.060</td>\n",
       "      <td>0.271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>struc_c</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.645</td>\n",
       "      <td>0.519</td>\n",
       "      <td></td>\n",
       "      <td>-0.180</td>\n",
       "      <td>0.356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>C_constraint:struc_c</td>\n",
       "      <td>-1.654</td>\n",
       "      <td>0.210</td>\n",
       "      <td>-7.874</td>\n",
       "      <td>3.44e-15</td>\n",
       "      <td>***</td>\n",
       "      <td>-2.066</td>\n",
       "      <td>-1.242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>age</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.003</td>\n",
       "      <td>1.193</td>\n",
       "      <td>0.233</td>\n",
       "      <td></td>\n",
       "      <td>-0.002</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ideology_1_7_last</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.281</td>\n",
       "      <td>0.778</td>\n",
       "      <td></td>\n",
       "      <td>-0.034</td>\n",
       "      <td>0.045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     term   coef  se_HC3       t         p  \\\n",
       "0                               Intercept  3.512   0.135  26.049  1.4e-149   \n",
       "1                     CAT(gender)[T.Male]  0.059   0.042   1.396     0.163   \n",
       "2               CAT(region)[T.SeoulMetro]  0.074   0.042   1.756    0.0791   \n",
       "3            CAT(education)[T.College2yr]  0.116   0.054   2.142    0.0322   \n",
       "4                 CAT(education)[T.Grad+] -0.155   0.079  -1.974    0.0484   \n",
       "5            CAT(education)[T.HighSchool]  0.226   0.063   3.601  0.000318   \n",
       "6          CAT(education)[T.MiddleOrLess]  0.045   0.108   0.415     0.678   \n",
       "7          CAT(monthly_income_band)[T.Q2] -0.048   0.082  -0.586     0.558   \n",
       "8          CAT(monthly_income_band)[T.Q3] -0.042   0.085  -0.497     0.619   \n",
       "9          CAT(monthly_income_band)[T.Q4]  0.005   0.085   0.056     0.955   \n",
       "10         CAT(monthly_income_band)[T.Q5] -0.039   0.091  -0.433     0.665   \n",
       "11         CAT(monthly_income_band)[T.Q6] -0.051   0.086  -0.592     0.554   \n",
       "12         CAT(monthly_income_band)[T.Q7] -0.022   0.089  -0.251     0.802   \n",
       "13         CAT(monthly_income_band)[T.Q8]  0.092   0.100   0.918     0.358   \n",
       "14                    CAT(homeowner)[T.1]  0.001   0.044   0.022     0.982   \n",
       "15           CAT(marital_status)[T.Other]  0.038   0.056   0.679     0.497   \n",
       "16          CAT(marital_status)[T.Single]  0.029   0.051   0.580     0.562   \n",
       "17               CAT(union_member)[T.Yes] -0.057   0.070  -0.817     0.414   \n",
       "18          CAT(employment_type)[T.Other]  0.118   0.110   1.075     0.282   \n",
       "19        CAT(employment_type)[T.Regular]  0.061   0.047   1.294     0.196   \n",
       "20  CAT(employment_type)[T.Self-employed]  0.065   0.076   0.865     0.387   \n",
       "21                           C_constraint  0.226   0.061   3.723  0.000197   \n",
       "22                              S_support -0.212   0.057  -3.704  0.000213   \n",
       "23                 C_constraint:S_support  0.105   0.084   1.245     0.213   \n",
       "24                                struc_c  0.088   0.137   0.645     0.519   \n",
       "25                   C_constraint:struc_c -1.654   0.210  -7.874  3.44e-15   \n",
       "26                                    age  0.003   0.003   1.193     0.233   \n",
       "27                      ideology_1_7_last  0.006   0.020   0.281     0.778   \n",
       "\n",
       "    sig  ci_low  ci_high  \n",
       "0   ***   3.248    3.777  \n",
       "1        -0.024    0.141  \n",
       "2     *  -0.009    0.157  \n",
       "3    **   0.010    0.223  \n",
       "4    **  -0.309   -0.001  \n",
       "5   ***   0.103    0.350  \n",
       "6        -0.166    0.256  \n",
       "7        -0.210    0.113  \n",
       "8        -0.208    0.124  \n",
       "9        -0.162    0.172  \n",
       "10       -0.217    0.139  \n",
       "11       -0.220    0.118  \n",
       "12       -0.197    0.152  \n",
       "13       -0.104    0.287  \n",
       "14       -0.085    0.087  \n",
       "15       -0.071    0.147  \n",
       "16       -0.070    0.129  \n",
       "17       -0.195    0.080  \n",
       "18       -0.097    0.334  \n",
       "19       -0.031    0.152  \n",
       "20       -0.083    0.214  \n",
       "21  ***   0.107    0.346  \n",
       "22  ***  -0.324   -0.100  \n",
       "23       -0.060    0.271  \n",
       "24       -0.180    0.356  \n",
       "25  ***  -2.066   -1.242  \n",
       "26       -0.002    0.008  \n",
       "27       -0.034    0.045  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## Figure 5 (Social essentiality) — **H5 (Social essentiality)**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Spec A (baseline): DV ~ Constraint*Support + controls**  \n",
       "N = 2,000 | R² = 0.367 | Adj. R² = 0.359"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "*Key terms:*"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>C_constraint</td>\n",
       "      <td>1.181</td>\n",
       "      <td>0.050</td>\n",
       "      <td>23.627</td>\n",
       "      <td>2.05e-123</td>\n",
       "      <td>***</td>\n",
       "      <td>1.083</td>\n",
       "      <td>1.279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>S_support</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>0.048</td>\n",
       "      <td>-0.076</td>\n",
       "      <td>0.94</td>\n",
       "      <td></td>\n",
       "      <td>-0.099</td>\n",
       "      <td>0.091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>C_constraint:S_support</td>\n",
       "      <td>-0.067</td>\n",
       "      <td>0.070</td>\n",
       "      <td>-0.967</td>\n",
       "      <td>0.334</td>\n",
       "      <td></td>\n",
       "      <td>-0.204</td>\n",
       "      <td>0.069</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      term   coef  se_HC3       t          p  sig  ci_low  \\\n",
       "21            C_constraint  1.181   0.050  23.627  2.05e-123  ***   1.083   \n",
       "22               S_support -0.004   0.048  -0.076       0.94       -0.099   \n",
       "23  C_constraint:S_support -0.067   0.070  -0.967      0.334       -0.204   \n",
       "\n",
       "    ci_high  \n",
       "21    1.279  \n",
       "22    0.091  \n",
       "23    0.069  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Full results — Spec A**  \n",
       "N = 2,000 | R² = 0.367 | Adj. R² = 0.359"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
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     "output_type": "display_data"
    },
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>4.108</td>\n",
       "      <td>0.116</td>\n",
       "      <td>35.530</td>\n",
       "      <td>1.71e-276</td>\n",
       "      <td>***</td>\n",
       "      <td>3.881</td>\n",
       "      <td>4.335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAT(gender)[T.Male]</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.441</td>\n",
       "      <td>0.659</td>\n",
       "      <td></td>\n",
       "      <td>-0.053</td>\n",
       "      <td>0.083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CAT(region)[T.SeoulMetro]</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.746</td>\n",
       "      <td>0.456</td>\n",
       "      <td></td>\n",
       "      <td>-0.042</td>\n",
       "      <td>0.094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAT(education)[T.College2yr]</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.046</td>\n",
       "      <td>1.388</td>\n",
       "      <td>0.165</td>\n",
       "      <td></td>\n",
       "      <td>-0.026</td>\n",
       "      <td>0.154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAT(education)[T.Grad+]</td>\n",
       "      <td>-0.016</td>\n",
       "      <td>0.061</td>\n",
       "      <td>-0.260</td>\n",
       "      <td>0.795</td>\n",
       "      <td></td>\n",
       "      <td>-0.135</td>\n",
       "      <td>0.104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CAT(education)[T.HighSchool]</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.051</td>\n",
       "      <td>1.380</td>\n",
       "      <td>0.167</td>\n",
       "      <td></td>\n",
       "      <td>-0.030</td>\n",
       "      <td>0.172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CAT(education)[T.MiddleOrLess]</td>\n",
       "      <td>0.084</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.932</td>\n",
       "      <td>0.352</td>\n",
       "      <td></td>\n",
       "      <td>-0.093</td>\n",
       "      <td>0.261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q2]</td>\n",
       "      <td>-0.077</td>\n",
       "      <td>0.068</td>\n",
       "      <td>-1.141</td>\n",
       "      <td>0.254</td>\n",
       "      <td></td>\n",
       "      <td>-0.210</td>\n",
       "      <td>0.056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q3]</td>\n",
       "      <td>-0.016</td>\n",
       "      <td>0.068</td>\n",
       "      <td>-0.232</td>\n",
       "      <td>0.817</td>\n",
       "      <td></td>\n",
       "      <td>-0.149</td>\n",
       "      <td>0.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q4]</td>\n",
       "      <td>-0.068</td>\n",
       "      <td>0.070</td>\n",
       "      <td>-0.979</td>\n",
       "      <td>0.328</td>\n",
       "      <td></td>\n",
       "      <td>-0.205</td>\n",
       "      <td>0.069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q5]</td>\n",
       "      <td>-0.028</td>\n",
       "      <td>0.075</td>\n",
       "      <td>-0.375</td>\n",
       "      <td>0.708</td>\n",
       "      <td></td>\n",
       "      <td>-0.175</td>\n",
       "      <td>0.119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q6]</td>\n",
       "      <td>-0.045</td>\n",
       "      <td>0.073</td>\n",
       "      <td>-0.625</td>\n",
       "      <td>0.532</td>\n",
       "      <td></td>\n",
       "      <td>-0.188</td>\n",
       "      <td>0.097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q7]</td>\n",
       "      <td>-0.090</td>\n",
       "      <td>0.073</td>\n",
       "      <td>-1.221</td>\n",
       "      <td>0.222</td>\n",
       "      <td></td>\n",
       "      <td>-0.234</td>\n",
       "      <td>0.054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q8]</td>\n",
       "      <td>-0.117</td>\n",
       "      <td>0.077</td>\n",
       "      <td>-1.513</td>\n",
       "      <td>0.13</td>\n",
       "      <td></td>\n",
       "      <td>-0.269</td>\n",
       "      <td>0.035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CAT(homeowner)[T.1]</td>\n",
       "      <td>-0.017</td>\n",
       "      <td>0.038</td>\n",
       "      <td>-0.446</td>\n",
       "      <td>0.655</td>\n",
       "      <td></td>\n",
       "      <td>-0.090</td>\n",
       "      <td>0.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CAT(marital_status)[T.Other]</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.045</td>\n",
       "      <td>1.051</td>\n",
       "      <td>0.293</td>\n",
       "      <td></td>\n",
       "      <td>-0.041</td>\n",
       "      <td>0.136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CAT(marital_status)[T.Single]</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.987</td>\n",
       "      <td>0.323</td>\n",
       "      <td></td>\n",
       "      <td>-0.042</td>\n",
       "      <td>0.126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAT(union_member)[T.Yes]</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.220</td>\n",
       "      <td>0.826</td>\n",
       "      <td></td>\n",
       "      <td>-0.107</td>\n",
       "      <td>0.134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CAT(employment_type)[T.Other]</td>\n",
       "      <td>-0.065</td>\n",
       "      <td>0.084</td>\n",
       "      <td>-0.777</td>\n",
       "      <td>0.437</td>\n",
       "      <td></td>\n",
       "      <td>-0.231</td>\n",
       "      <td>0.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CAT(employment_type)[T.Regular]</td>\n",
       "      <td>-0.018</td>\n",
       "      <td>0.039</td>\n",
       "      <td>-0.461</td>\n",
       "      <td>0.645</td>\n",
       "      <td></td>\n",
       "      <td>-0.095</td>\n",
       "      <td>0.059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CAT(employment_type)[T.Self-employed]</td>\n",
       "      <td>-0.035</td>\n",
       "      <td>0.060</td>\n",
       "      <td>-0.579</td>\n",
       "      <td>0.563</td>\n",
       "      <td></td>\n",
       "      <td>-0.153</td>\n",
       "      <td>0.083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>C_constraint</td>\n",
       "      <td>1.181</td>\n",
       "      <td>0.050</td>\n",
       "      <td>23.627</td>\n",
       "      <td>2.05e-123</td>\n",
       "      <td>***</td>\n",
       "      <td>1.083</td>\n",
       "      <td>1.279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>S_support</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>0.048</td>\n",
       "      <td>-0.076</td>\n",
       "      <td>0.94</td>\n",
       "      <td></td>\n",
       "      <td>-0.099</td>\n",
       "      <td>0.091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>C_constraint:S_support</td>\n",
       "      <td>-0.067</td>\n",
       "      <td>0.070</td>\n",
       "      <td>-0.967</td>\n",
       "      <td>0.334</td>\n",
       "      <td></td>\n",
       "      <td>-0.204</td>\n",
       "      <td>0.069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>age</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.002</td>\n",
       "      <td>-0.406</td>\n",
       "      <td>0.685</td>\n",
       "      <td></td>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ideology_1_7_last</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.772</td>\n",
       "      <td>0.44</td>\n",
       "      <td></td>\n",
       "      <td>-0.019</td>\n",
       "      <td>0.044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     term   coef  se_HC3       t          p  \\\n",
       "0                               Intercept  4.108   0.116  35.530  1.71e-276   \n",
       "1                     CAT(gender)[T.Male]  0.015   0.035   0.441      0.659   \n",
       "2               CAT(region)[T.SeoulMetro]  0.026   0.035   0.746      0.456   \n",
       "3            CAT(education)[T.College2yr]  0.064   0.046   1.388      0.165   \n",
       "4                 CAT(education)[T.Grad+] -0.016   0.061  -0.260      0.795   \n",
       "5            CAT(education)[T.HighSchool]  0.071   0.051   1.380      0.167   \n",
       "6          CAT(education)[T.MiddleOrLess]  0.084   0.090   0.932      0.352   \n",
       "7          CAT(monthly_income_band)[T.Q2] -0.077   0.068  -1.141      0.254   \n",
       "8          CAT(monthly_income_band)[T.Q3] -0.016   0.068  -0.232      0.817   \n",
       "9          CAT(monthly_income_band)[T.Q4] -0.068   0.070  -0.979      0.328   \n",
       "10         CAT(monthly_income_band)[T.Q5] -0.028   0.075  -0.375      0.708   \n",
       "11         CAT(monthly_income_band)[T.Q6] -0.045   0.073  -0.625      0.532   \n",
       "12         CAT(monthly_income_band)[T.Q7] -0.090   0.073  -1.221      0.222   \n",
       "13         CAT(monthly_income_band)[T.Q8] -0.117   0.077  -1.513       0.13   \n",
       "14                    CAT(homeowner)[T.1] -0.017   0.038  -0.446      0.655   \n",
       "15           CAT(marital_status)[T.Other]  0.047   0.045   1.051      0.293   \n",
       "16          CAT(marital_status)[T.Single]  0.042   0.043   0.987      0.323   \n",
       "17               CAT(union_member)[T.Yes]  0.014   0.061   0.220      0.826   \n",
       "18          CAT(employment_type)[T.Other] -0.065   0.084  -0.777      0.437   \n",
       "19        CAT(employment_type)[T.Regular] -0.018   0.039  -0.461      0.645   \n",
       "20  CAT(employment_type)[T.Self-employed] -0.035   0.060  -0.579      0.563   \n",
       "21                           C_constraint  1.181   0.050  23.627  2.05e-123   \n",
       "22                              S_support -0.004   0.048  -0.076       0.94   \n",
       "23                 C_constraint:S_support -0.067   0.070  -0.967      0.334   \n",
       "24                                    age -0.001   0.002  -0.406      0.685   \n",
       "25                      ideology_1_7_last  0.013   0.016   0.772       0.44   \n",
       "\n",
       "    sig  ci_low  ci_high  \n",
       "0   ***   3.881    4.335  \n",
       "1        -0.053    0.083  \n",
       "2        -0.042    0.094  \n",
       "3        -0.026    0.154  \n",
       "4        -0.135    0.104  \n",
       "5        -0.030    0.172  \n",
       "6        -0.093    0.261  \n",
       "7        -0.210    0.056  \n",
       "8        -0.149    0.118  \n",
       "9        -0.205    0.069  \n",
       "10       -0.175    0.119  \n",
       "11       -0.188    0.097  \n",
       "12       -0.234    0.054  \n",
       "13       -0.269    0.035  \n",
       "14       -0.090    0.057  \n",
       "15       -0.041    0.136  \n",
       "16       -0.042    0.126  \n",
       "17       -0.107    0.134  \n",
       "18       -0.231    0.100  \n",
       "19       -0.095    0.059  \n",
       "20       -0.153    0.083  \n",
       "21  ***   1.083    1.279  \n",
       "22       -0.099    0.091  \n",
       "23       -0.204    0.069  \n",
       "24       -0.005    0.003  \n",
       "25       -0.019    0.044  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Spec B (Norm moderation): + Norm + Constraint×Norm**  \n",
       "N = 2,000 | R² = 0.367 | Adj. R² = 0.358"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "*Key terms:*"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>C_constraint</td>\n",
       "      <td>1.182</td>\n",
       "      <td>0.050</td>\n",
       "      <td>23.615</td>\n",
       "      <td>2.69e-123</td>\n",
       "      <td>***</td>\n",
       "      <td>1.084</td>\n",
       "      <td>1.280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>S_support</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>0.049</td>\n",
       "      <td>-0.088</td>\n",
       "      <td>0.93</td>\n",
       "      <td></td>\n",
       "      <td>-0.099</td>\n",
       "      <td>0.091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>C_constraint:S_support</td>\n",
       "      <td>-0.068</td>\n",
       "      <td>0.070</td>\n",
       "      <td>-0.973</td>\n",
       "      <td>0.33</td>\n",
       "      <td></td>\n",
       "      <td>-0.204</td>\n",
       "      <td>0.069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>C_constraint:norm_c</td>\n",
       "      <td>-0.021</td>\n",
       "      <td>0.031</td>\n",
       "      <td>-0.675</td>\n",
       "      <td>0.499</td>\n",
       "      <td></td>\n",
       "      <td>-0.083</td>\n",
       "      <td>0.040</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      term   coef  se_HC3       t          p  sig  ci_low  \\\n",
       "21            C_constraint  1.182   0.050  23.615  2.69e-123  ***   1.084   \n",
       "22               S_support -0.004   0.049  -0.088       0.93       -0.099   \n",
       "23  C_constraint:S_support -0.068   0.070  -0.973       0.33       -0.204   \n",
       "25     C_constraint:norm_c -0.021   0.031  -0.675      0.499       -0.083   \n",
       "\n",
       "    ci_high  \n",
       "21    1.280  \n",
       "22    0.091  \n",
       "23    0.069  \n",
       "25    0.040  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Full results — Spec B**  \n",
       "N = 2,000 | R² = 0.367 | Adj. R² = 0.358"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>4.131</td>\n",
       "      <td>0.116</td>\n",
       "      <td>35.637</td>\n",
       "      <td>3.79e-278</td>\n",
       "      <td>***</td>\n",
       "      <td>3.904</td>\n",
       "      <td>4.358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAT(gender)[T.Male]</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.380</td>\n",
       "      <td>0.704</td>\n",
       "      <td></td>\n",
       "      <td>-0.055</td>\n",
       "      <td>0.081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CAT(region)[T.SeoulMetro]</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.715</td>\n",
       "      <td>0.474</td>\n",
       "      <td></td>\n",
       "      <td>-0.043</td>\n",
       "      <td>0.093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAT(education)[T.College2yr]</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.047</td>\n",
       "      <td>1.161</td>\n",
       "      <td>0.246</td>\n",
       "      <td></td>\n",
       "      <td>-0.037</td>\n",
       "      <td>0.146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAT(education)[T.Grad+]</td>\n",
       "      <td>-0.007</td>\n",
       "      <td>0.062</td>\n",
       "      <td>-0.107</td>\n",
       "      <td>0.915</td>\n",
       "      <td></td>\n",
       "      <td>-0.127</td>\n",
       "      <td>0.114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CAT(education)[T.HighSchool]</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.053</td>\n",
       "      <td>1.001</td>\n",
       "      <td>0.317</td>\n",
       "      <td></td>\n",
       "      <td>-0.051</td>\n",
       "      <td>0.157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CAT(education)[T.MiddleOrLess]</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.692</td>\n",
       "      <td>0.489</td>\n",
       "      <td></td>\n",
       "      <td>-0.117</td>\n",
       "      <td>0.245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q2]</td>\n",
       "      <td>-0.080</td>\n",
       "      <td>0.068</td>\n",
       "      <td>-1.186</td>\n",
       "      <td>0.236</td>\n",
       "      <td></td>\n",
       "      <td>-0.213</td>\n",
       "      <td>0.052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q3]</td>\n",
       "      <td>-0.016</td>\n",
       "      <td>0.068</td>\n",
       "      <td>-0.242</td>\n",
       "      <td>0.809</td>\n",
       "      <td></td>\n",
       "      <td>-0.150</td>\n",
       "      <td>0.117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q4]</td>\n",
       "      <td>-0.070</td>\n",
       "      <td>0.070</td>\n",
       "      <td>-1.006</td>\n",
       "      <td>0.314</td>\n",
       "      <td></td>\n",
       "      <td>-0.207</td>\n",
       "      <td>0.066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q5]</td>\n",
       "      <td>-0.029</td>\n",
       "      <td>0.075</td>\n",
       "      <td>-0.389</td>\n",
       "      <td>0.697</td>\n",
       "      <td></td>\n",
       "      <td>-0.176</td>\n",
       "      <td>0.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q6]</td>\n",
       "      <td>-0.048</td>\n",
       "      <td>0.072</td>\n",
       "      <td>-0.661</td>\n",
       "      <td>0.508</td>\n",
       "      <td></td>\n",
       "      <td>-0.190</td>\n",
       "      <td>0.094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q7]</td>\n",
       "      <td>-0.091</td>\n",
       "      <td>0.073</td>\n",
       "      <td>-1.234</td>\n",
       "      <td>0.217</td>\n",
       "      <td></td>\n",
       "      <td>-0.235</td>\n",
       "      <td>0.053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q8]</td>\n",
       "      <td>-0.118</td>\n",
       "      <td>0.077</td>\n",
       "      <td>-1.530</td>\n",
       "      <td>0.126</td>\n",
       "      <td></td>\n",
       "      <td>-0.270</td>\n",
       "      <td>0.033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CAT(homeowner)[T.1]</td>\n",
       "      <td>-0.017</td>\n",
       "      <td>0.038</td>\n",
       "      <td>-0.440</td>\n",
       "      <td>0.66</td>\n",
       "      <td></td>\n",
       "      <td>-0.090</td>\n",
       "      <td>0.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CAT(marital_status)[T.Other]</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.045</td>\n",
       "      <td>1.069</td>\n",
       "      <td>0.285</td>\n",
       "      <td></td>\n",
       "      <td>-0.040</td>\n",
       "      <td>0.137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CAT(marital_status)[T.Single]</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.344</td>\n",
       "      <td></td>\n",
       "      <td>-0.044</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAT(union_member)[T.Yes]</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.226</td>\n",
       "      <td>0.821</td>\n",
       "      <td></td>\n",
       "      <td>-0.107</td>\n",
       "      <td>0.134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CAT(employment_type)[T.Other]</td>\n",
       "      <td>-0.063</td>\n",
       "      <td>0.084</td>\n",
       "      <td>-0.743</td>\n",
       "      <td>0.457</td>\n",
       "      <td></td>\n",
       "      <td>-0.228</td>\n",
       "      <td>0.102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CAT(employment_type)[T.Regular]</td>\n",
       "      <td>-0.019</td>\n",
       "      <td>0.039</td>\n",
       "      <td>-0.490</td>\n",
       "      <td>0.624</td>\n",
       "      <td></td>\n",
       "      <td>-0.096</td>\n",
       "      <td>0.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CAT(employment_type)[T.Self-employed]</td>\n",
       "      <td>-0.037</td>\n",
       "      <td>0.061</td>\n",
       "      <td>-0.616</td>\n",
       "      <td>0.538</td>\n",
       "      <td></td>\n",
       "      <td>-0.156</td>\n",
       "      <td>0.081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>C_constraint</td>\n",
       "      <td>1.182</td>\n",
       "      <td>0.050</td>\n",
       "      <td>23.615</td>\n",
       "      <td>2.69e-123</td>\n",
       "      <td>***</td>\n",
       "      <td>1.084</td>\n",
       "      <td>1.280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>S_support</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>0.049</td>\n",
       "      <td>-0.088</td>\n",
       "      <td>0.93</td>\n",
       "      <td></td>\n",
       "      <td>-0.099</td>\n",
       "      <td>0.091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>C_constraint:S_support</td>\n",
       "      <td>-0.068</td>\n",
       "      <td>0.070</td>\n",
       "      <td>-0.973</td>\n",
       "      <td>0.33</td>\n",
       "      <td></td>\n",
       "      <td>-0.204</td>\n",
       "      <td>0.069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>norm_c</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>0.022</td>\n",
       "      <td>-0.401</td>\n",
       "      <td>0.688</td>\n",
       "      <td></td>\n",
       "      <td>-0.052</td>\n",
       "      <td>0.034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>C_constraint:norm_c</td>\n",
       "      <td>-0.021</td>\n",
       "      <td>0.031</td>\n",
       "      <td>-0.675</td>\n",
       "      <td>0.499</td>\n",
       "      <td></td>\n",
       "      <td>-0.083</td>\n",
       "      <td>0.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>age</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.002</td>\n",
       "      <td>-0.496</td>\n",
       "      <td>0.62</td>\n",
       "      <td></td>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ideology_1_7_last</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.697</td>\n",
       "      <td>0.486</td>\n",
       "      <td></td>\n",
       "      <td>-0.021</td>\n",
       "      <td>0.043</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     term   coef  se_HC3       t          p  \\\n",
       "0                               Intercept  4.131   0.116  35.637  3.79e-278   \n",
       "1                     CAT(gender)[T.Male]  0.013   0.035   0.380      0.704   \n",
       "2               CAT(region)[T.SeoulMetro]  0.025   0.035   0.715      0.474   \n",
       "3            CAT(education)[T.College2yr]  0.054   0.047   1.161      0.246   \n",
       "4                 CAT(education)[T.Grad+] -0.007   0.062  -0.107      0.915   \n",
       "5            CAT(education)[T.HighSchool]  0.053   0.053   1.001      0.317   \n",
       "6          CAT(education)[T.MiddleOrLess]  0.064   0.092   0.692      0.489   \n",
       "7          CAT(monthly_income_band)[T.Q2] -0.080   0.068  -1.186      0.236   \n",
       "8          CAT(monthly_income_band)[T.Q3] -0.016   0.068  -0.242      0.809   \n",
       "9          CAT(monthly_income_band)[T.Q4] -0.070   0.070  -1.006      0.314   \n",
       "10         CAT(monthly_income_band)[T.Q5] -0.029   0.075  -0.389      0.697   \n",
       "11         CAT(monthly_income_band)[T.Q6] -0.048   0.072  -0.661      0.508   \n",
       "12         CAT(monthly_income_band)[T.Q7] -0.091   0.073  -1.234      0.217   \n",
       "13         CAT(monthly_income_band)[T.Q8] -0.118   0.077  -1.530      0.126   \n",
       "14                    CAT(homeowner)[T.1] -0.017   0.038  -0.440       0.66   \n",
       "15           CAT(marital_status)[T.Other]  0.048   0.045   1.069      0.285   \n",
       "16          CAT(marital_status)[T.Single]  0.041   0.043   0.946      0.344   \n",
       "17               CAT(union_member)[T.Yes]  0.014   0.061   0.226      0.821   \n",
       "18          CAT(employment_type)[T.Other] -0.063   0.084  -0.743      0.457   \n",
       "19        CAT(employment_type)[T.Regular] -0.019   0.039  -0.490      0.624   \n",
       "20  CAT(employment_type)[T.Self-employed] -0.037   0.061  -0.616      0.538   \n",
       "21                           C_constraint  1.182   0.050  23.615  2.69e-123   \n",
       "22                              S_support -0.004   0.049  -0.088       0.93   \n",
       "23                 C_constraint:S_support -0.068   0.070  -0.973       0.33   \n",
       "24                                 norm_c -0.009   0.022  -0.401      0.688   \n",
       "25                    C_constraint:norm_c -0.021   0.031  -0.675      0.499   \n",
       "26                                    age -0.001   0.002  -0.496       0.62   \n",
       "27                      ideology_1_7_last  0.011   0.016   0.697      0.486   \n",
       "\n",
       "    sig  ci_low  ci_high  \n",
       "0   ***   3.904    4.358  \n",
       "1        -0.055    0.081  \n",
       "2        -0.043    0.093  \n",
       "3        -0.037    0.146  \n",
       "4        -0.127    0.114  \n",
       "5        -0.051    0.157  \n",
       "6        -0.117    0.245  \n",
       "7        -0.213    0.052  \n",
       "8        -0.150    0.117  \n",
       "9        -0.207    0.066  \n",
       "10       -0.176    0.118  \n",
       "11       -0.190    0.094  \n",
       "12       -0.235    0.053  \n",
       "13       -0.270    0.033  \n",
       "14       -0.090    0.057  \n",
       "15       -0.040    0.137  \n",
       "16       -0.044    0.125  \n",
       "17       -0.107    0.134  \n",
       "18       -0.228    0.102  \n",
       "19       -0.096    0.057  \n",
       "20       -0.156    0.081  \n",
       "21  ***   1.084    1.280  \n",
       "22       -0.099    0.091  \n",
       "23       -0.204    0.069  \n",
       "24       -0.052    0.034  \n",
       "25       -0.083    0.040  \n",
       "26       -0.005    0.003  \n",
       "27       -0.021    0.043  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Spec C (Structural moderation): + LowCarbonProx + Constraint×LowCarbonProx**  \n",
       "N = 2,000 | R² = 0.367 | Adj. R² = 0.358"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "*Key terms:*"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>C_constraint</td>\n",
       "      <td>1.182</td>\n",
       "      <td>0.050</td>\n",
       "      <td>23.598</td>\n",
       "      <td>4.07e-123</td>\n",
       "      <td>***</td>\n",
       "      <td>1.083</td>\n",
       "      <td>1.280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>S_support</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>0.049</td>\n",
       "      <td>-0.082</td>\n",
       "      <td>0.935</td>\n",
       "      <td></td>\n",
       "      <td>-0.099</td>\n",
       "      <td>0.091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>C_constraint:S_support</td>\n",
       "      <td>-0.068</td>\n",
       "      <td>0.070</td>\n",
       "      <td>-0.969</td>\n",
       "      <td>0.333</td>\n",
       "      <td></td>\n",
       "      <td>-0.204</td>\n",
       "      <td>0.069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>C_constraint:struc_c</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0.548</td>\n",
       "      <td>0.584</td>\n",
       "      <td></td>\n",
       "      <td>-0.245</td>\n",
       "      <td>0.435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      term   coef  se_HC3       t          p  sig  ci_low  \\\n",
       "21            C_constraint  1.182   0.050  23.598  4.07e-123  ***   1.083   \n",
       "22               S_support -0.004   0.049  -0.082      0.935       -0.099   \n",
       "23  C_constraint:S_support -0.068   0.070  -0.969      0.333       -0.204   \n",
       "25    C_constraint:struc_c  0.095   0.173   0.548      0.584       -0.245   \n",
       "\n",
       "    ci_high  \n",
       "21    1.280  \n",
       "22    0.091  \n",
       "23    0.069  \n",
       "25    0.435  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Full results — Spec C**  \n",
       "N = 2,000 | R² = 0.367 | Adj. R² = 0.358"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
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    },
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>coef</th>\n",
       "      <th>se_HC3</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>sig</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>4.108</td>\n",
       "      <td>0.116</td>\n",
       "      <td>35.481</td>\n",
       "      <td>9.58e-276</td>\n",
       "      <td>***</td>\n",
       "      <td>3.881</td>\n",
       "      <td>4.335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAT(gender)[T.Male]</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.428</td>\n",
       "      <td>0.668</td>\n",
       "      <td></td>\n",
       "      <td>-0.053</td>\n",
       "      <td>0.083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CAT(region)[T.SeoulMetro]</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.749</td>\n",
       "      <td>0.454</td>\n",
       "      <td></td>\n",
       "      <td>-0.042</td>\n",
       "      <td>0.094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAT(education)[T.College2yr]</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.046</td>\n",
       "      <td>1.381</td>\n",
       "      <td>0.167</td>\n",
       "      <td></td>\n",
       "      <td>-0.027</td>\n",
       "      <td>0.155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAT(education)[T.Grad+]</td>\n",
       "      <td>-0.016</td>\n",
       "      <td>0.061</td>\n",
       "      <td>-0.257</td>\n",
       "      <td>0.797</td>\n",
       "      <td></td>\n",
       "      <td>-0.135</td>\n",
       "      <td>0.104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CAT(education)[T.HighSchool]</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.052</td>\n",
       "      <td>1.381</td>\n",
       "      <td>0.167</td>\n",
       "      <td></td>\n",
       "      <td>-0.030</td>\n",
       "      <td>0.172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CAT(education)[T.MiddleOrLess]</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.921</td>\n",
       "      <td>0.357</td>\n",
       "      <td></td>\n",
       "      <td>-0.094</td>\n",
       "      <td>0.261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q2]</td>\n",
       "      <td>-0.077</td>\n",
       "      <td>0.068</td>\n",
       "      <td>-1.140</td>\n",
       "      <td>0.254</td>\n",
       "      <td></td>\n",
       "      <td>-0.211</td>\n",
       "      <td>0.056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q3]</td>\n",
       "      <td>-0.016</td>\n",
       "      <td>0.068</td>\n",
       "      <td>-0.237</td>\n",
       "      <td>0.813</td>\n",
       "      <td></td>\n",
       "      <td>-0.150</td>\n",
       "      <td>0.117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q4]</td>\n",
       "      <td>-0.068</td>\n",
       "      <td>0.070</td>\n",
       "      <td>-0.974</td>\n",
       "      <td>0.33</td>\n",
       "      <td></td>\n",
       "      <td>-0.205</td>\n",
       "      <td>0.069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q5]</td>\n",
       "      <td>-0.028</td>\n",
       "      <td>0.075</td>\n",
       "      <td>-0.374</td>\n",
       "      <td>0.708</td>\n",
       "      <td></td>\n",
       "      <td>-0.175</td>\n",
       "      <td>0.119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q6]</td>\n",
       "      <td>-0.045</td>\n",
       "      <td>0.073</td>\n",
       "      <td>-0.614</td>\n",
       "      <td>0.539</td>\n",
       "      <td></td>\n",
       "      <td>-0.187</td>\n",
       "      <td>0.098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q7]</td>\n",
       "      <td>-0.090</td>\n",
       "      <td>0.074</td>\n",
       "      <td>-1.227</td>\n",
       "      <td>0.22</td>\n",
       "      <td></td>\n",
       "      <td>-0.234</td>\n",
       "      <td>0.054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CAT(monthly_income_band)[T.Q8]</td>\n",
       "      <td>-0.116</td>\n",
       "      <td>0.077</td>\n",
       "      <td>-1.500</td>\n",
       "      <td>0.134</td>\n",
       "      <td></td>\n",
       "      <td>-0.268</td>\n",
       "      <td>0.036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CAT(homeowner)[T.1]</td>\n",
       "      <td>-0.017</td>\n",
       "      <td>0.038</td>\n",
       "      <td>-0.460</td>\n",
       "      <td>0.645</td>\n",
       "      <td></td>\n",
       "      <td>-0.091</td>\n",
       "      <td>0.056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CAT(marital_status)[T.Other]</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.045</td>\n",
       "      <td>1.034</td>\n",
       "      <td>0.301</td>\n",
       "      <td></td>\n",
       "      <td>-0.042</td>\n",
       "      <td>0.135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CAT(marital_status)[T.Single]</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.974</td>\n",
       "      <td>0.33</td>\n",
       "      <td></td>\n",
       "      <td>-0.042</td>\n",
       "      <td>0.126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAT(union_member)[T.Yes]</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.238</td>\n",
       "      <td>0.812</td>\n",
       "      <td></td>\n",
       "      <td>-0.106</td>\n",
       "      <td>0.135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CAT(employment_type)[T.Other]</td>\n",
       "      <td>-0.065</td>\n",
       "      <td>0.084</td>\n",
       "      <td>-0.769</td>\n",
       "      <td>0.442</td>\n",
       "      <td></td>\n",
       "      <td>-0.230</td>\n",
       "      <td>0.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CAT(employment_type)[T.Regular]</td>\n",
       "      <td>-0.018</td>\n",
       "      <td>0.039</td>\n",
       "      <td>-0.454</td>\n",
       "      <td>0.65</td>\n",
       "      <td></td>\n",
       "      <td>-0.094</td>\n",
       "      <td>0.059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CAT(employment_type)[T.Self-employed]</td>\n",
       "      <td>-0.035</td>\n",
       "      <td>0.060</td>\n",
       "      <td>-0.572</td>\n",
       "      <td>0.567</td>\n",
       "      <td></td>\n",
       "      <td>-0.153</td>\n",
       "      <td>0.084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>C_constraint</td>\n",
       "      <td>1.182</td>\n",
       "      <td>0.050</td>\n",
       "      <td>23.598</td>\n",
       "      <td>4.07e-123</td>\n",
       "      <td>***</td>\n",
       "      <td>1.083</td>\n",
       "      <td>1.280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>S_support</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>0.049</td>\n",
       "      <td>-0.082</td>\n",
       "      <td>0.935</td>\n",
       "      <td></td>\n",
       "      <td>-0.099</td>\n",
       "      <td>0.091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>C_constraint:S_support</td>\n",
       "      <td>-0.068</td>\n",
       "      <td>0.070</td>\n",
       "      <td>-0.969</td>\n",
       "      <td>0.333</td>\n",
       "      <td></td>\n",
       "      <td>-0.204</td>\n",
       "      <td>0.069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>struc_c</td>\n",
       "      <td>-0.057</td>\n",
       "      <td>0.116</td>\n",
       "      <td>-0.494</td>\n",
       "      <td>0.621</td>\n",
       "      <td></td>\n",
       "      <td>-0.284</td>\n",
       "      <td>0.170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>C_constraint:struc_c</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0.548</td>\n",
       "      <td>0.584</td>\n",
       "      <td></td>\n",
       "      <td>-0.245</td>\n",
       "      <td>0.435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>age</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.002</td>\n",
       "      <td>-0.417</td>\n",
       "      <td>0.677</td>\n",
       "      <td></td>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ideology_1_7_last</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.794</td>\n",
       "      <td>0.427</td>\n",
       "      <td></td>\n",
       "      <td>-0.019</td>\n",
       "      <td>0.045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     term   coef  se_HC3       t          p  \\\n",
       "0                               Intercept  4.108   0.116  35.481  9.58e-276   \n",
       "1                     CAT(gender)[T.Male]  0.015   0.035   0.428      0.668   \n",
       "2               CAT(region)[T.SeoulMetro]  0.026   0.035   0.749      0.454   \n",
       "3            CAT(education)[T.College2yr]  0.064   0.046   1.381      0.167   \n",
       "4                 CAT(education)[T.Grad+] -0.016   0.061  -0.257      0.797   \n",
       "5            CAT(education)[T.HighSchool]  0.071   0.052   1.381      0.167   \n",
       "6          CAT(education)[T.MiddleOrLess]  0.083   0.090   0.921      0.357   \n",
       "7          CAT(monthly_income_band)[T.Q2] -0.077   0.068  -1.140      0.254   \n",
       "8          CAT(monthly_income_band)[T.Q3] -0.016   0.068  -0.237      0.813   \n",
       "9          CAT(monthly_income_band)[T.Q4] -0.068   0.070  -0.974       0.33   \n",
       "10         CAT(monthly_income_band)[T.Q5] -0.028   0.075  -0.374      0.708   \n",
       "11         CAT(monthly_income_band)[T.Q6] -0.045   0.073  -0.614      0.539   \n",
       "12         CAT(monthly_income_band)[T.Q7] -0.090   0.074  -1.227       0.22   \n",
       "13         CAT(monthly_income_band)[T.Q8] -0.116   0.077  -1.500      0.134   \n",
       "14                    CAT(homeowner)[T.1] -0.017   0.038  -0.460      0.645   \n",
       "15           CAT(marital_status)[T.Other]  0.047   0.045   1.034      0.301   \n",
       "16          CAT(marital_status)[T.Single]  0.042   0.043   0.974       0.33   \n",
       "17               CAT(union_member)[T.Yes]  0.015   0.061   0.238      0.812   \n",
       "18          CAT(employment_type)[T.Other] -0.065   0.084  -0.769      0.442   \n",
       "19        CAT(employment_type)[T.Regular] -0.018   0.039  -0.454       0.65   \n",
       "20  CAT(employment_type)[T.Self-employed] -0.035   0.060  -0.572      0.567   \n",
       "21                           C_constraint  1.182   0.050  23.598  4.07e-123   \n",
       "22                              S_support -0.004   0.049  -0.082      0.935   \n",
       "23                 C_constraint:S_support -0.068   0.070  -0.969      0.333   \n",
       "24                                struc_c -0.057   0.116  -0.494      0.621   \n",
       "25                   C_constraint:struc_c  0.095   0.173   0.548      0.584   \n",
       "26                                    age -0.001   0.002  -0.417      0.677   \n",
       "27                      ideology_1_7_last  0.013   0.016   0.794      0.427   \n",
       "\n",
       "    sig  ci_low  ci_high  \n",
       "0   ***   3.881    4.335  \n",
       "1        -0.053    0.083  \n",
       "2        -0.042    0.094  \n",
       "3        -0.027    0.155  \n",
       "4        -0.135    0.104  \n",
       "5        -0.030    0.172  \n",
       "6        -0.094    0.261  \n",
       "7        -0.211    0.056  \n",
       "8        -0.150    0.117  \n",
       "9        -0.205    0.069  \n",
       "10       -0.175    0.119  \n",
       "11       -0.187    0.098  \n",
       "12       -0.234    0.054  \n",
       "13       -0.268    0.036  \n",
       "14       -0.091    0.056  \n",
       "15       -0.042    0.135  \n",
       "16       -0.042    0.126  \n",
       "17       -0.106    0.135  \n",
       "18       -0.230    0.100  \n",
       "19       -0.094    0.059  \n",
       "20       -0.153    0.084  \n",
       "21  ***   1.083    1.280  \n",
       "22       -0.099    0.091  \n",
       "23       -0.204    0.069  \n",
       "24       -0.284    0.170  \n",
       "25       -0.245    0.435  \n",
       "26       -0.005    0.003  \n",
       "27       -0.019    0.045  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "✅ **Done. 5 hypotheses × 3 specifications = 15 full regression tables displayed.**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ============================================================\n",
    "DATA_DIR  = r\"E:\\\"\n",
    "DATA_FILE = os.path.join(DATA_DIR, \"df.xlsx\")\n",
    "\n",
    "df = pd.read_excel(DATA_FILE)\n",
    "\n",
    "# ============================================================\n",
    "# 1) Variables\n",
    "# ============================================================\n",
    "P = \"C_constraint\"\n",
    "S = \"S_support\"\n",
    "\n",
    "DVs = {\n",
    "    \"Figure 1 (Legitimacy)\": \"O1_legitimacy\",\n",
    "    \"Figure 2 (Government responsibility)\": \"O2_gov_responsibility\",\n",
    "    \"Figure 3 (Transition support)\": \"O3_transition_support\",\n",
    "    \"Figure 4 (Regulatory flexibility)\": \"O4_reg_flexibility\",\n",
    "    \"Figure 5 (Social essentiality)\": \"O5_social_essentiality\",\n",
    "}\n",
    "\n",
    "# DV -> Hypothesis mapping (your intended logic)\n",
    "DV_TO_HYP = {\n",
    "    \"O1_legitimacy\": \"H1 (Legitimacy)\",\n",
    "    \"O2_gov_responsibility\": \"H2 (Government responsibility)\",\n",
    "    \"O3_transition_support\": \"H3 (Transition support)\",\n",
    "    \"O4_reg_flexibility\": \"H4 (Regulatory flexibility)\",\n",
    "    \"O5_social_essentiality\": \"H5 (Social essentiality)\",\n",
    "}\n",
    "\n",
    "# ---- Norm\n",
    "norm_items = [\"N1_env_norm\",\"N2_env_norm\",\"N3_env_norm\",\"N4_env_norm\",\"N5_env_norm\"]\n",
    "if \"Norm_index\" in df.columns:\n",
    "    df[\"norm_index\"] = df[\"Norm_index\"]\n",
    "else:\n",
    "    df[\"norm_index\"] = df[norm_items].mean(axis=1)\n",
    "df[\"norm_c\"] = df[\"norm_index\"] - df[\"norm_index\"].mean()\n",
    "\n",
    "# ---- Structural position\n",
    "df[\"low_carbon_prox\"] = df[\"low_carbon_proximity_0_10\"] / 10.0\n",
    "df[\"low_carbon_prox\"] = df[\"low_carbon_prox\"].fillna(df[\"low_carbon_prox\"].mean())\n",
    "df[\"struc_c\"] = df[\"low_carbon_prox\"] - df[\"low_carbon_prox\"].mean()\n",
    "\n",
    "# ---- Income band if missing\n",
    "if \"monthly_income_band\" not in df.columns:\n",
    "    df[\"monthly_income_band\"] = pd.qcut(\n",
    "        df[\"income_millionKRW\"], q=8, labels=[f\"Q{i}\" for i in range(1, 9)]\n",
    "    )\n",
    "\n",
    "if \"union_member\" in df.columns:\n",
    "    df[\"union_member\"] = df[\"union_member\"].fillna(\"No\")\n",
    "\n",
    "# ============================================================\n",
    "# 2) Controls\n",
    "# ============================================================\n",
    "CAT = pb.C\n",
    "\n",
    "CONTROL_TERMS = [\n",
    "    \"age\",\n",
    "    \"CAT(gender)\",\n",
    "    \"CAT(region)\",\n",
    "    \"CAT(education)\",\n",
    "    \"CAT(monthly_income_band)\",\n",
    "    \"CAT(homeowner)\",\n",
    "    \"CAT(marital_status)\",\n",
    "    \"CAT(union_member)\",\n",
    "    \"CAT(employment_type)\",\n",
    "    \"ideology_1_7_last\",\n",
    "]\n",
    "CONTROLS = \" + \".join(CONTROL_TERMS)\n",
    "\n",
    "# ============================================================\n",
    "# 3) Helpers\n",
    "# ============================================================\n",
    "def fit_ols(formula, data):\n",
    "    return smf.ols(formula, data=data).fit(cov_type=\"HC3\")\n",
    "\n",
    "def full_table(m):\n",
    "    out = pd.DataFrame({\n",
    "        \"term\": m.params.index,\n",
    "        \"coef\": m.params.values,\n",
    "        \"se_HC3\": m.bse.values,\n",
    "        \"t\": m.tvalues.values,\n",
    "        \"p\": m.pvalues.values\n",
    "    })\n",
    "    out[\"ci_low\"] = out[\"coef\"] - 1.96*out[\"se_HC3\"]\n",
    "    out[\"ci_high\"] = out[\"coef\"] + 1.96*out[\"se_HC3\"]\n",
    "    return out\n",
    "\n",
    "def add_sig_stars(p):\n",
    "    if p < 0.01: return \"***\"\n",
    "    if p < 0.05: return \"**\"\n",
    "    if p < 0.1: return \"*\"\n",
    "    return \"\"\n",
    "\n",
    "def pretty(df_in, digits=3):\n",
    "    df2 = df_in.copy()\n",
    "    df2[\"sig\"] = df2[\"p\"].apply(add_sig_stars)\n",
    "    for c in [\"coef\",\"se_HC3\",\"t\",\"ci_low\",\"ci_high\"]:\n",
    "        df2[c] = df2[c].map(lambda x: round(float(x), digits))\n",
    "    df2[\"p\"] = df2[\"p\"].map(lambda x: f\"{x:.3g}\")\n",
    "    return df2[[\"term\",\"coef\",\"se_HC3\",\"t\",\"p\",\"sig\",\"ci_low\",\"ci_high\"]]\n",
    "\n",
    "KEY_TERMS = [P, S, f\"{P}:{S}\", f\"{P}:norm_c\", f\"{P}:struc_c\"]\n",
    "\n",
    "def model_stats_line(m):\n",
    "    return f\"N = {int(m.nobs):,} | R² = {m.rsquared:.3f} | Adj. R² = {m.rsquared_adj:.3f}\"\n",
    "\n",
    "def show_model_key_terms(title, m):\n",
    "    display(Markdown(f\"**{title}**  \\n{model_stats_line(m)}\"))\n",
    "    tbl = pretty(full_table(m))\n",
    "    display(Markdown(\"*Key terms:*\"))\n",
    "    display(tbl[tbl[\"term\"].isin(KEY_TERMS)])\n",
    "\n",
    "def show_model(title, m):\n",
    "    display(Markdown(f\"**{title}**  \\n{model_stats_line(m)}\"))\n",
    "    display(pretty(full_table(m)))\n",
    "\n",
    "# ============================================================\n",
    "# 4) Run + Display (5 DVs × 3 specs = 15 tables)\n",
    "#     NOTE: All 3 specs per DV correspond to the SAME hypothesis.\n",
    "# ============================================================\n",
    "for fig_name, dv in DVs.items():\n",
    "    hyp = DV_TO_HYP.get(dv, \"Hypothesis\")\n",
    "    display(Markdown(f\"## {fig_name} — **{hyp}**\"))\n",
    "\n",
    "    # Spec A: baseline (Constraint*Support)\n",
    "    fA = f\"{dv} ~ {P}*{S} + {CONTROLS}\"\n",
    "    mA = fit_ols(fA, df)\n",
    "    show_model_key_terms(\"Spec A (baseline): DV ~ Constraint*Support + controls\", mA)\n",
    "    show_model(\"Full results — Spec A\", mA)\n",
    "\n",
    "    # Spec B: add Norm moderation\n",
    "    fB = f\"{dv} ~ {P}*{S} + norm_c + {P}:norm_c + {CONTROLS}\"\n",
    "    mB = fit_ols(fB, df)\n",
    "    show_model_key_terms(\"Spec B (Norm moderation): + Norm + Constraint×Norm\", mB)\n",
    "    show_model(\"Full results — Spec B\", mB)\n",
    "\n",
    "    # Spec C: add Structural moderation\n",
    "    fC = f\"{dv} ~ {P}*{S} + struc_c + {P}:struc_c + {CONTROLS}\"\n",
    "    mC = fit_ols(fC, df)\n",
    "    show_model_key_terms(\"Spec C (Structural moderation): + LowCarbonProx + Constraint×LowCarbonProx\", mC)\n",
    "    show_model(\"Full results — Spec C\", mC)\n",
    "\n",
    "display(Markdown(\"✅ **Done. 5 hypotheses × 3 specifications = 15 full regression tables displayed.**\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7977549-a3c1-4220-94be-1698cf5d476d",
   "metadata": {},
   "source": [
    "## 3. Triple interaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "66aeb6fe-3bde-412b-8bc1-df2be84cd788",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "## Triple interaction coefficient only (Constraint × Norm × LowCarbonProx)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DV</th>\n",
       "      <th>coef</th>\n",
       "      <th>se</th>\n",
       "      <th>p</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "      <th>N</th>\n",
       "      <th>R2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Legitimacy</td>\n",
       "      <td>0.101957</td>\n",
       "      <td>0.150834</td>\n",
       "      <td>0.499068</td>\n",
       "      <td>-0.193677</td>\n",
       "      <td>0.397591</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.237133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Government responsibility</td>\n",
       "      <td>0.042782</td>\n",
       "      <td>0.141469</td>\n",
       "      <td>0.762336</td>\n",
       "      <td>-0.234497</td>\n",
       "      <td>0.320062</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.208824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Transition support</td>\n",
       "      <td>-0.076031</td>\n",
       "      <td>0.146628</td>\n",
       "      <td>0.604087</td>\n",
       "      <td>-0.363422</td>\n",
       "      <td>0.211359</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.386886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Regulatory flexibility</td>\n",
       "      <td>0.036536</td>\n",
       "      <td>0.155008</td>\n",
       "      <td>0.813662</td>\n",
       "      <td>-0.267280</td>\n",
       "      <td>0.340352</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.202137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Social essentiality</td>\n",
       "      <td>-0.076867</td>\n",
       "      <td>0.133727</td>\n",
       "      <td>0.565424</td>\n",
       "      <td>-0.338973</td>\n",
       "      <td>0.185239</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.393366</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          DV      coef        se         p    ci_low  \\\n",
       "0                 Legitimacy  0.101957  0.150834  0.499068 -0.193677   \n",
       "1  Government responsibility  0.042782  0.141469  0.762336 -0.234497   \n",
       "2         Transition support -0.076031  0.146628  0.604087 -0.363422   \n",
       "3     Regulatory flexibility  0.036536  0.155008  0.813662 -0.267280   \n",
       "4        Social essentiality -0.076867  0.133727  0.565424 -0.338973   \n",
       "\n",
       "    ci_high     N        R2  \n",
       "0  0.397591  2000  0.237133  \n",
       "1  0.320062  2000  0.208824  \n",
       "2  0.211359  2000  0.386886  \n",
       "3  0.340352  2000  0.202137  \n",
       "4  0.185239  2000  0.393366  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x425 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: E:\\불평등 연구\\데이터\\69_환경_도덕\\_appendixB_triple_coefplots\\Appendix_triple_interaction_coefplot.png\n"
     ]
    }
   ],
   "source": [
    "DATA_DIR  = r\"E:\\\"\n",
    "DATA_FILE = os.path.join(DATA_DIR, \"df.xlsx\")   # <- your file\n",
    "\n",
    "OUT_DIR = os.path.join(DATA_DIR, \"_appendixB_triple_coefplots\")\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "df = pd.read_excel(DATA_FILE)\n",
    "\n",
    "# ============================================================\n",
    "# VARIABLES\n",
    "# ============================================================\n",
    "# Vignette factors\n",
    "P = \"C_constraint\"     # (analog of Presence)\n",
    "S = \"S_support\"\n",
    "\n",
    "# DVs\n",
    "DVs = {\n",
    "    \"Legitimacy\": \"O1_legitimacy\",\n",
    "    \"Government responsibility\": \"O2_gov_responsibility\",\n",
    "    \"Transition support\": \"O3_transition_support\",\n",
    "    \"Regulatory flexibility\": \"O4_reg_flexibility\",\n",
    "    \"Social essentiality\": \"O5_social_essentiality\",\n",
    "}\n",
    "\n",
    "# ---- Norm index (same as main)\n",
    "norm_items = [\"N1_env_norm\",\"N2_env_norm\",\"N3_env_norm\",\"N4_env_norm\",\"N5_env_norm\"]\n",
    "if \"Norm_index\" in df.columns:\n",
    "    df[\"norm_index\"] = df[\"Norm_index\"]\n",
    "else:\n",
    "    df[\"norm_index\"] = df[norm_items].mean(axis=1)\n",
    "df[\"norm_c\"] = df[\"norm_index\"] - df[\"norm_index\"].mean()\n",
    "\n",
    "# ---- Structural position (0–10 -> 0–1), mean impute missing, center\n",
    "df[\"struc\"] = df[\"low_carbon_proximity_0_10\"] / 10.0\n",
    "df[\"struc\"] = df[\"struc\"].fillna(df[\"struc\"].mean())\n",
    "df[\"struc_c\"] = df[\"struc\"] - df[\"struc\"].mean()\n",
    "\n",
    "# ---- Income band if missing (to match main controls)\n",
    "if \"monthly_income_band\" not in df.columns:\n",
    "    df[\"monthly_income_band\"] = pd.qcut(\n",
    "        df[\"income_millionKRW\"], q=8, labels=[f\"Q{i}\" for i in range(1, 9)]\n",
    "    )\n",
    "\n",
    "# ---- Union NA handling (keep N=2000)\n",
    "if \"union_member\" in df.columns:\n",
    "    df[\"union_member\"] = df[\"union_member\"].fillna(\"No\")\n",
    "\n",
    "# ============================================================\n",
    "# CONTROLS (same baseline set used in main figures)\n",
    "#   * Use CAT() to avoid patsy C() collision in notebooks\n",
    "# ============================================================\n",
    "CAT = pb.C\n",
    "CONTROL_TERMS_MAIN = [\n",
    "    \"age\",\n",
    "    \"CAT(gender)\",\n",
    "    \"CAT(region)\",\n",
    "    \"CAT(education)\",\n",
    "    \"CAT(monthly_income_band)\",\n",
    "    \"CAT(homeowner)\",\n",
    "    \"CAT(marital_status)\",\n",
    "    \"CAT(union_member)\",\n",
    "    \"CAT(employment_type)\",\n",
    "    \"ideology_1_7_last\",\n",
    "]\n",
    "CONTROLS_MAIN = \" + \".join(CONTROL_TERMS_MAIN)\n",
    "\n",
    "# ============================================================\n",
    "# HELPERS\n",
    "# ============================================================\n",
    "def fit_ols(formula, data):\n",
    "    return smf.ols(formula, data=data).fit(cov_type=\"HC3\")\n",
    "\n",
    "def find_triple_term(model, P_name=P, norm_name=\"norm_c\", struc_name=\"struc_c\"):\n",
    "    \"\"\"\n",
    "    statsmodels/patsy may reorder interaction terms.\n",
    "    Find any term that contains all three components.\n",
    "    \"\"\"\n",
    "    for t in model.params.index:\n",
    "        if (P_name in t) and (norm_name in t) and (struc_name in t):\n",
    "            return t\n",
    "    return None\n",
    "\n",
    "def p_fmt(p):\n",
    "    if p < 0.001:\n",
    "        return \"<0.001\"\n",
    "    return f\"{p:.3f}\"\n",
    "\n",
    "# ============================================================\n",
    "# RUN TRIPLE MODELS AND COLLECT ONLY THE TRIPLE TERM\n",
    "# ============================================================\n",
    "rows = []\n",
    "for dv_label, dv_col in DVs.items():\n",
    "    # Full triple specification (Appendix check)\n",
    "    # Includes P*Support and full three-way among P, Norm, Structural position\n",
    "    # NOTE: We include main and lower-order terms by using norm_c*struc_c*P\n",
    "    f = f\"{dv_col} ~ {P}*{S} + norm_c*struc_c*{P} + {CONTROLS_MAIN}\"\n",
    "    m = fit_ols(f, df)\n",
    "\n",
    "    triple = find_triple_term(m)\n",
    "    if triple is None:\n",
    "        continue\n",
    "\n",
    "    coef = float(m.params[triple])\n",
    "    se = float(m.bse[triple])\n",
    "    p = float(m.pvalues[triple])\n",
    "\n",
    "    rows.append({\n",
    "        \"DV\": dv_label,\n",
    "        \"term\": triple,\n",
    "        \"coef\": coef,\n",
    "        \"se\": se,\n",
    "        \"ci_low\": coef - 1.96 * se,\n",
    "        \"ci_high\": coef + 1.96 * se,\n",
    "        \"p\": p,\n",
    "        \"N\": int(m.nobs),\n",
    "        \"R2\": float(m.rsquared)\n",
    "    })\n",
    "\n",
    "triple_df = pd.DataFrame(rows)\n",
    "\n",
    "display(Markdown(\"## Triple interaction coefficient only (Constraint × Norm × LowCarbonProx)\"))\n",
    "if not triple_df.empty:\n",
    "    display(triple_df[[\"DV\",\"coef\",\"se\",\"p\",\"ci_low\",\"ci_high\",\"N\",\"R2\"]])\n",
    "else:\n",
    "    display(Markdown(\"No triple term found. Check variable names and model specification.\"))\n",
    "\n",
    "# ============================================================\n",
    "# COEFFICIENT PLOT (single figure, 5 rows)\n",
    "# Style matches main figures: red points, black CI, red dashed zero line\n",
    "# Annotate with coef and p next to point\n",
    "# ============================================================\n",
    "if not triple_df.empty:\n",
    "    # Keep DV order stable\n",
    "    dv_order = list(DVs.keys())\n",
    "    triple_df[\"DV\"] = pd.Categorical(triple_df[\"DV\"], categories=dv_order, ordered=True)\n",
    "    triple_df = triple_df.sort_values(\"DV\").reset_index(drop=True)\n",
    "\n",
    "    y = np.arange(len(triple_df))\n",
    "    coefs = triple_df[\"coef\"].to_numpy()\n",
    "    ci_low = triple_df[\"ci_low\"].to_numpy()\n",
    "    ci_high = triple_df[\"ci_high\"].to_numpy()\n",
    "    pvals = triple_df[\"p\"].to_numpy()\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(12, max(3.5, 0.65 * len(triple_df) + 1.0)))\n",
    "\n",
    "    ax.hlines(y, ci_low, ci_high, color=\"black\", linewidth=2)\n",
    "    ax.plot(coefs, y, marker=\"o\", linestyle=\"None\", color=\"red\", markersize=9)\n",
    "    ax.axvline(0, color=\"red\", linestyle=\"--\", linewidth=1.6)\n",
    "\n",
    "    ax.set_yticks(y)\n",
    "    ax.set_yticklabels(triple_df[\"DV\"].tolist(), fontsize=13)\n",
    "    ax.invert_yaxis()\n",
    "\n",
    "    xmin = min(ci_low.min(), 0.0)\n",
    "    xmax = max(ci_high.max(), 0.0)\n",
    "    span = xmax - xmin + 1e-9\n",
    "    ax.set_xlim(xmin - 0.20 * span, xmax + 0.20 * span)\n",
    "\n",
    "    dx = 0.04 * span\n",
    "    for i, (c, p) in enumerate(zip(coefs, pvals)):\n",
    "        txt = f\"{c:.3f}, p={p_fmt(p)}\"\n",
    "        x_text = c + (dx if c >= 0 else -dx)\n",
    "        ax.annotate(\n",
    "            txt,\n",
    "            xy=(x_text, i),\n",
    "            xytext=(0, 2),\n",
    "            textcoords=\"offset points\",\n",
    "            ha=\"left\",\n",
    "            va=\"bottom\",\n",
    "            fontsize=12\n",
    "        )\n",
    "\n",
    "    ax.set_title(\"Triple interaction term\", fontsize=16, pad=12)\n",
    "    ax.set_xlabel(\"Coefficient (95% CI)\", fontsize=13, labelpad=10)\n",
    "    ax.tick_params(axis=\"x\", labelsize=13, pad=6)\n",
    "    ax.tick_params(axis=\"y\", pad=8)\n",
    "\n",
    "    plt.tight_layout(pad=1.2)\n",
    "\n",
    "    outpath = os.path.join(OUT_DIR, \"Appendix_triple_interaction_coefplot.png\")\n",
    "    plt.savefig(outpath, dpi=240, bbox_inches=\"tight\")\n",
    "    plt.show()\n",
    "    plt.close()\n",
    "\n",
    "    print(\"Saved:\", outpath)\n",
    "else:\n",
    "    print(\"No triple term found. Check variable names and model specification.\")"
   ]
  },
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